Receiving method and receiver for satellite-based automatic identification systems

ABSTRACT

A method for demodulating a received signal relating to a sequence of transmitted symbols that have been modulated by continuous phase modulation includes normalizing samples of a sequence of samples generated from the received signal, to obtain a normalized sequence of samples, wherein an amplitude of each sample of the normalized sequence of samples has an absolute value equal to unity; estimating, on the basis of the normalized sequence of samples, a time offset and a frequency offset of the received signal, and using the estimated time offset and the estimated frequency offset for compensating the normalized sequence of samples for the time and frequency offsets, to obtain a compensated sequence of samples; and determining a sequence of symbols corresponding to the transmitted sequence of symbols on the basis of the compensated sequence of samples. Also disclosed is a receiver for demodulating a received signal relating to a sequence of transmitted symbols that have been modulated by continuous phase modulation.

BACKGROUND

Technical Field

The present disclosure relates to a method for demodulating a receivedsignal relating to a sequence of transmitted symbols that have beenmodulated by continuous phase modulation (CPM) and to an apparatus(receiver) for demodulating a received signal relating to a sequence oftransmitted symbols that have been modulated by CPM.

The disclosure is particularly, though not exclusively, applicable todemodulating a received signal relating to a sequence of transmittedsymbols that represent one or more messages in an AutomaticIdentification System (AIS). Embodiments of the disclosure areparticularly suited to be applied to an AIS receiver in a spacecraft,such as a satellite.

Description of the Related Art

An AIS provides identification and location information to naval vesselsand shore stations with the aim of exchanging data including informationon position, identification, course and speed. This allows naval vesselsto anticipate and thus avoid collisions with other naval vessels bymeans of continuous traffic monitoring with several navigation aids. Inaddition, AIS also offers important naval vessel monitoring services tocoastal guards or to search and rescue organizations.

The AIS is based on broadcasting of fixed-length digital messages in aTime Division Multiple Access (TDMA) framework. Individual AIS messagescorresponding to sequences of symbols to be transmitted are modulated bymeans of CPM. Each naval vessel equipped with an AIS apparatusbroadcasts information (data) in small slots of 26.67 ms. In each ofthese slots a message of 256 bits is transmitted at a rate of 9600 b/susing a binary Gaussian Minimum Shift Keying (GMSK) modulation over twoVery High Frequency (VHF) carriers. Nearby AIS emitters synchronize witheach other in order to avoid packet collisions, i.e., avoid emission ofmore than one packet in the same time slot by different emitters (timeslots are defined globally on the basis of a common temporal referenceprovided by GPS). As a result, Self-Organized Time Division MultipleAccess (SOTDMA) regions are formed. Each SOTDMA region (SOTDMA cell) isdesigned to cope with path delays not longer than 12 bits, whichtranslates into a maximum range of about 200 nautical miles, buttypically the radio frequency coverage is limited to about 40 nauticalmiles. Within this range all the naval vessels in visibility transmit inaccordance with the SOTDMA protocol which ensures that packet collisionsbetween bursts transmitted by different naval vessels are prevented.

Attempts to improve handling of hazardous cargo, security and counteringillegal operations have led to the introduction of satellite based AIS.Satellite based AIS enables detecting and tracking naval vessels atdistances from coastlines that are larger than can be accomplished bynormal terrestrial VHF communications, so that naval vessels may bedetected at very long distanced from shores. In particular, a LEO (lowearth orbit) constellation of small-size satellites, with an altituderanging from 600 km to 1000 km, can provide global coverage. Eachsatellite is provided with an on-board small VHF antenna with a field ofview spanning over a few thousands of nautical miles and thus comprisingup to several hundreds of SOTDMA cells.

Satellite-based AIS, however, has to face with additional technicalchallenges that were not considered in the original AIS standard: AISmessages from naval vessels belonging to different SOTDMA cells are notsynchronized and therefore can collide with each other, satellite motionwith respect to the emitters induces a significant Doppler shift of thecarrier frequency, the signal to noise ratio is lower than interrestrial AIS, and the relative propagation channel delay among thepopulation of naval vessels in visibility at any given time is muchhigher than for terrestrial AIS.

These problems have been addressed in patent document EP 2 315 366 A1which relates to a receiver architecture for satellite-based AISsystems. This receiver architecture is composed of three zonaldemodulators that process different (but overlapping) frequencybandwidths, as is shown in FIG. 1. The frequency band of each of the AISchannels is sub-divided into three sub-bands, and each of the sub-bandsis processed by a corresponding one of the zonal demodulators, therebyexploiting the carrier Doppler diversity for obtaining an estimate ofthe distance to the respective transmitter and the corresponding pathdelay. Interference resilient message synchronization is performed bymeans of Cyclic Redundancy Check (CRC)-aided techniques. Multiplecolliding messages are detected by means of digital re-modulation andcancellation of successfully decoded messages.

However, the above solution to the problems faced by satellite-based AISturns out to be in need of improvement as regards packet error rate(PER) and bit error rate (BER), especially in the presence of heavytraffic leading to heavy interference between AIS messages received atthe AIS receiver, and in the presence of AIS messages containing longsequence of zeros. The latter typically occur for latitudes and/orlongitudes of the transmitting naval vessel close to zero degrees, i.e.,close to the equator and/or the zero median, e.g., in the gulf ofGuinea.

BRIEF SUMMARY

Embodiments of the present disclosure are designed to address thelimitations of the prior art discussed above. Embodiments of thedisclosure improve the performance of a receiver in a satellite-basedAIS system as regards PER and BER in the presence of interference.Further, embodiments of the disclosure improve the performance of areceiver in a satellite-based AIS system as regards PER and BER forlongitudes and altitudes close to zero degrees.

In at least one embodiment, disclosed herein is a method fordemodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulation and areceiver for demodulating a received signal relating to a sequence oftransmitted symbols that have been modulated by continuous phasemodulation, having the features of the respective independent claims.Preferred embodiments of the disclosure are described in the dependentclaims.

According to an aspect of the present disclosure, a method fordemodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulationcomprises the steps of: normalizing the sequence of samples generatedfrom the received signal, to obtain a normalized sequence of samples,wherein an amplitude of each sample of the normalized sequence ofsamples has an absolute value (i.e., magnitude) equal to unity;estimating, on the basis of the normalized sequence of samples, a timeoffset and a frequency offset of the received signal, and using theestimated time offset and the estimated frequency offset forcompensating the normalized sequence of samples for the time andfrequency offsets, to obtain a compensated sequence of samples; anddetermining a sequence of symbols corresponding to the transmittedsequence of symbols on the basis of the compensated sequence of samples.The method may further comprise a step of generating the sequence ofsamples from the received signal.

The above method may be applied to each zonal demodulator of the priorart AIS receiver disclosed in EP 2 315 366 A1. As the present inventorshave found out, introducing the step of normalizing the samples to unityresults in a significant increase of performance as regards PER and BERin the presence of heavy traffic (i.e., in the presence of stronginterference). Contrary to intuition, the introduction of thisadditional step in demodulating the signal results in an overallimprovement of performance and efficiency. Any decrease of performancein the absence of the above aggravating circumstances is more thanbalanced by the significant increase in performance in the presence ofthese circumstances. Moreover, limiting the samples to unit absolutevalue reduces the overall computational burden for subsequent processingsteps, which results in an overall increase of processing efficiencyand/or gives leeway for implementing more effective, even if slightlyless efficient processes at the pre-detection stage, the detectionstage, and/or the post-processing stage of the AIS receiver.

Preferably, the estimate of the time offset and the estimate of thefrequency offset are determined using a feed-forward algorithm thatinvolves performing an auto-correlation of a sequence of samples inputto the algorithm. Further preferably, estimating the time offset and thefrequency offset involves: filtering the normalized sequence of samplesusing a low-pass filter to obtain a filtered sequence of samples;determining the estimate of the time offset on the basis of a firstresult obtained by auto-correlating the filtered sequence of samples;determining the estimate of the frequency offset on the basis of asecond result obtained by auto-correlating the filtered sequence ofsamples or a first sequence of samples derived from the normalizedsequence of samples; interpolating the normalized sequence of samples ora second sequence of samples derived from the normalized sequence ofsamples on the basis of the estimate of the time offset, in order tocorrect for the time offset; and compensating the normalized sequence ofsamples or a third sequence of samples derived from the normalizedsequence of samples for the frequency offset using the estimate of thefrequency offset, to obtain the compensated sequence of samples.

By this measure, reliable and accurate estimates of the time offset andfrequency offset can be determined, and the received signal, or thesequence of samples derived therefrom can be compensated for the effectof the time offset and the frequency offset of the received signal.Therein, the time offset corresponds to an offset of first bits ofrespective packets of the received signal with respect to a fixed timeframe of the receiver (e.g., a time frame provided by GPS), and thefrequency offset corresponds to an offset between the actual frequencyof the received signal from the respective carrier frequency at whichthe signal had been transmitted (in the satellite-based AIS thefrequency offset is due to a Doppler shift). After compensation, thesequence of samples can be subjected to packet detection (packetdecoding), the reliability of which is enhanced by having access to thedetermined reliable and accurate estimates of the time offset and thefrequency offset. Here, the more accurate the estimates of the timeoffset and the frequency offset, the lower the resulting BER (andcorrespondingly, also PER).

A particular advantage is achieved if estimating the time offset and thefrequency offset involves: filtering the normalized sequence of samplesusing a first low-pass filter to obtain a first filtered sequence ofsamples; determining the estimate of the time offset on the basis of afirst result obtained by auto-correlating the first filtered sequence ofsamples; determining a first estimate of the frequency offset on thebasis of the first result; and compensating the normalized sequence ofsamples for the frequency offset using the first estimate of thefrequency offset, to obtain a first compensated sequence of samples.Determination of the first estimate of the frequency offset may befurther based on the estimate of the time offset. Preferably, estimatingthe time offset and the frequency offset further involves: filtering thefirst compensated sequence of samples using a second low-pass filter toobtain a second filtered sequence of samples; determining a secondestimate of the frequency offset on the basis of a second resultobtained by auto-correlating the second filtered sequence of samples;compensating the first compensated sequence of samples for the frequencyoffset using the second estimate of the frequency offset, to obtain asecond compensated sequence of samples; and interpolating the secondcompensated sequence of samples on the basis of the estimate of the timeoffset to obtain the compensated sequence of samples. Determination ofthe second estimate of the frequency offset may be further based on theestimate of the time offset.

Accordingly, the pre-detection synchronization stage (i.e., the stageresponsible for estimating the time offset and the frequency offset andperforming appropriate compensation of the signal or sequence ofsamples) according to the application comprises two stages of frequencyoffset estimation. The second stage of frequency offset estimationoperates on a sequence of samples compensated for the effect of thefrequency offset on the basis of a first estimate determined by thefirst stage of frequency offset estimation and thus can provide a moreaccurate estimate of the frequency offset. Therein, applying the firstand second stages of frequency offset estimation is particularlyefficient since said stages are applied to the normalized sequence ofsamples which comprises only samples having an absolute value of unity.Especially in filtering and auto-correlating, a significant enhancementin performance is achieved by the normalization. As it turns out, theinventive combination of providing a step of normalizing the sequence ofsamples and a step of estimating the frequency offset in a two-stageprocess is advantageous both with regard to overall performance andaccuracy of the resulting estimate of frequency estimation. In thisregard, the method disclosed herein has been found to be particularlyefficient in avoiding a biased frequency estimate.

Alternatively, estimating the time offset and the frequency offset mayinvolve: filtering the normalized sequence of samples using a firstlow-pass filter to obtain a first filtered sequence of samples;determining the estimate of the time offset on the basis of a firstresult obtained by auto-correlating the first filtered sequence ofsamples; and interpolating the normalized sequence of samples on thebasis of the estimate of the time offset to obtain an interpolatedsequence of samples. Estimating the time offset and the frequency offsetmay further involve: filtering the interpolated sequence of samplesusing a second low-pass filter to obtain a second filtered sequence ofsamples; down-sampling the second filtered sequence of samples to obtaina first down-sampled sequence of samples; determining a first estimateof the frequency offset on the basis of a second result obtained byauto-correlating the first down-sampled sequence of samples; andcompensating the interpolated sequence of samples for the frequencyoffset using the first estimate of the frequency offset, to obtain afirst compensated sequence of samples. Determination of the firstestimate of the frequency offset may be further based on the estimate ofthe time offset.

In addition to the above, estimating the time offset and the frequencyoffset may further involve: filtering the first compensated sequence ofsamples using a third low-pass filter to obtain a third filteredsequence of samples; determining a second estimate of the frequencyoffset on the basis of a third result obtained by auto-correlating thethird filtered sequence of samples; and compensating the firstcompensated sequence of samples for the frequency offset using thesecond estimate of the frequency offset, to obtain the compensatedsequence of samples. Determination of the second estimate of thefrequency offset may be further based on the estimate of the timeoffset. The first result may be obtained by applying a firstauto-correlation algorithm to the first filtered sequence of samples,and the second result may be obtained by applying the firstauto-correlation algorithm to the down-sampled sequence of samples. As apreferred alternative, the first result is obtained by applying a firstauto-correlation algorithm to the first filtered sequence of samples,and the second result is obtained by applying a second auto-correlationalgorithm that is different from the first auto-correlation algorithm tothe first down-sampled sequence of samples. Preferably, the third resultis obtained by applying the first auto-correlation algorithm to thethird filtered sequence of samples.

According to the present disclosure, either the same auto-correlationalgorithm can be employed as the first and second auto-correlationalgorithms, or different algorithms can be employed. A particularadvantage however has been found to result from employing differentalgorithms. Accordingly, e.g., a first coarse (and time-efficient)estimation of the frequency offset can be performed using a firstalgorithm, and a fine estimation of the frequency offset can bedetermined subsequently. As the inventors have found out, the decreasein overall accuracy of the estimate of the frequency offset compared toa case with two stages of fine estimation of the frequency offset isminimal, while this measure significantly increases the performance andefficiency both with regard to time and computational effort and reducesthe overall complexity of the corresponding receiver for demodulatingthe received signal.

As an alternative, in the above the first result may be obtained byapplying a first auto-correlation algorithm to the first filteredsequence of samples, the second result may be obtained by applying asecond auto-correlation algorithm that is different from the firstauto-correlation algorithm to the down-sampled sequence of samples, andthe first compensated sequence of samples may be the compensatedsequence of samples.

By appropriate choice of the second auto-correlation algorithm, a veryfast estimation of the frequency offset can be obtained, if needs be.

A particular advantage is achieved if in the step of determining thesequence of symbols, each of the determined symbols is a symbol that hasa highest probability of being identical to the correspondingtransmitted symbol.

According to another aspect of the disclosure, a method for demodulatinga received signal relating to a sequence of transmitted symbols thathave been modulated by continuous phase modulation comprises the stepsof: estimating a time offset and a frequency offset of the receivedsignal on the basis of a sequence of samples generated from the receivedsignal, and using the estimated time offset and the estimated frequencyoffset for compensating the sequence of samples for the time andfrequency offsets, to obtain a compensated sequence of samples; anddetermining a sequence of symbols corresponding to the transmittedsequence of symbols on the basis of the compensated sequence of samples,wherein each of the determined symbols is a symbol that has a highestprobability of being identical to the corresponding transmitted symbol.The method may further comprise a step of generating the sequence ofsamples from the received signal.

Accordingly, the method disclosed herein employs a Soft Input SoftOutput (SISO) algorithm in the detection stage. Instead of outputting asequence of bits (symbols) that has, as a whole, the highest probabilityof corresponding to the transmitted sequence, as is done in the priorart, now a determination based on a probability (likelihood) isperformed separately for each symbol. This is especially advantageous incase of interference between packets, which in the prior art may resultin incorrectly decoded packets. By contrast, according to the presentdisclosure, if a decoded packet or sequence of samples turns out to beincorrect, post-processing techniques considering individual symbols orpairs of symbols may be applied in order to obtain the correct packet orsequence. Clearly, such post-processing techniques are not possible inthe prior art, in which the decoded packet or sequence is treated as awhole.

It is further suggested that for each determined symbol, a probabilityof the determined symbol being identical to the correspondingtransmitted symbol is determined.

As indicated above, having knowledge of probabilities (likelihoods) ofindividual symbols being correct enables application of very efficientpost-processing techniques for correcting incorrectly decoded packets.For instance, if only few symbols of a packet are wrong, as is usuallythe case, the wrong symbols can be identified by referring to theindividual probabilities and identifying those symbols that have thelowest probabilities. Accordingly, wrong symbols can possibly becorrected, thereby obtaining a correctly decoded packet in an efficientmanner.

A further advantage is achieved if the method further comprises:generating a packet from the determined sequence of symbols, calculatinga checksum for the packet, and if the checksum indicates that the packethas not been decoded correctly, inverting a pair of symbols in thepacket, wherein the two symbols of the pair of symbols are separated bya further symbol.

Having at hand the probabilities (likelihoods) of the symbols of adecoded packet, those symbols in an incorrectly decoded packet that aremost likely wrong can be determined. By changing (inverting) the valuesof these symbols, then possibly a correct packet or sequence can beobtained. As the inventors have realized, errors in the decoded packetalmost always occur in couples (pairs) of symbols that are separated bya further symbol, i.e., in pairs of the form “p1|x|p2”, where “p1” and“p2” are the symbols of the pair, that are not necessarily identical,and “x” indicates a further symbol separating the symbols of the pair.Then, by simultaneously inverting the symbols of such pair, possibly acorrect packet can be obtained.

Preferably, the method further comprises: in the packet that has beenjudged as not decoded correctly, determining a first pair of symbolshaving the lowest probability of being identical to the correspondingtransmitted symbols, and inverting the symbols of the determined firstpair of symbols. As a further preferred alternative, the method furthercomprises: in the packet that has been judged as not decoded correctly,determining a first pair of symbols having the lowest probability ofbeing identical to the corresponding transmitted symbols and a secondpair of symbols having the next-to-lowest probability of being identicalto the corresponding transmitted symbols, and inverting, not necessarilyin this order, the symbols of the first pair only, the symbols of thesecond pair only, and the symbols of the first and second pairssimultaneously, until the checksum of the resulting packet indicatesthat the resulting packet has been decoded correctly. As a yet furtherpreferred alternative, the method comprises: for the packet that hasbeen judged as not decoded correctly, determining an error sequence onthe basis of the checksum and a pre-stored table indicating arelationship between checksum values and error sequences, and invertingpairs of symbols that are located in the packet at positions indicatedby the error sequence.

Typically, only few (i.e., one or two) pairs of symbols per packet areincorrect, so that the above method allows arriving at correctly decodedpackets in a very efficient manner. As the inventors have further foundout, the checksum of an incorrect packet is indicative of an errorpattern, which indicates the location of the incorrect pair(s) ofsymbols. By inverting the pair(s) of symbols indicated by the errorsequence, efficiency of post-processing can be further increased.

In the presently-disclosed method, the sequence of samples may have afirst ratio of samples per transmitted symbol, and the method mayfurther comprise: down-sampling the compensated sequence of samples toobtain a down-sampled sequence of samples, the down-sampled sequence ofsamples having a second ratio of samples per transmitted symbol lowerthan the first ratio of samples, and determining the sequence of symbolscorresponding to the transmitted sequence of symbols on the basis of thedown-sampled sequence of samples. Preferably, the first ratio is 3 ormore, and the second ratio is 1.

By this measure, efficiency of the decoding stage can be enhanced, whileat the same time accuracy of the estimation of both the time offset andthe frequency offset is increased. Using fewer samples per symbols fordetermining the sequence of symbols corresponding to the transmittedsequence of symbols reduces complexity of the method and thecorresponding receiver, while still optimal detection can be performed.

It is further suggested that the method further comprises: identifyingpackets of symbols that have been decoded correctly, cancelling saidcorrectly decoded packets from the sequence of symbols by subtracting,from the sequence of symbols, a reconstructed sequence of symbols thathas been reconstructed from said correctly decoded packets to obtain aninterference-cancelled sequence of symbols, and repeating theaforementioned steps for the interference-cancelled sequence of symbols.

For a satellite-based AIS, messages from individual transmitters arriveat the receiver out of synchronization with an internal time frame ofthe receiver (provided, e.g., by GPS), and may moreover overlap (i.e.,interfere) with each other. However, in case that one or more packetsfor a given time interval have been successfully decoded, these packetsmay be subtracted from the received signal or the sequence of samplesgenerated therefrom in the given time interval. Thereby, interference bythese successfully decoded packets is cancelled from the received signalor the sequence of samples generated therefrom, and further packets, thedecoding of which had failed previously due to interference (or decodingof which has not been attempted), may now be decoded. Thus, this measureincreases the ratio of successfully decoded packets in case ofinterference between packets of different transmitters, which especiallyoccurs in the case of satellite-based AIS.

An additional advantage is achieved if the method further comprises, ifdecoding a packet of symbols has failed, determining a reception timingat which the respective packet has been received, determining the fieldof view from which signals could have been received at the receptiontiming, obtaining a list of potential transmitters that have been in thefield of view at the reception timing, correlating, for each of thepotential transmitters, an identifier of the respective potentialtransmitter of the packet for which decoding has failed with said packetto obtain a correlation value, obtaining previously obtained datarelating to each of the potential transmitters for which the correlationvalue is above a predetermined threshold, and decoding the packet usingthe previously obtained data.

Typically, packets that are found to be decoded incorrectly, and thatalso cannot be corrected using post-processing techniques, arediscarded. However, these packets can possibly be decoded when takinginto account available a-priori information. While typical receivers donot possess sufficient computational resources for performing so-calleddata-aided decoding, incorrect packets may be transmitted to a remotesite having sufficient computational resources, such as an on-groundsite. Using available a-priori information that can be derived from orcorresponds to previously obtained information, a reliability of packetdecoding, i.e., a chance that a given packet is decoded correctly, canbe increased. In the case of the AIS, a-priori information is availablein the form of the Maritime Mobile Service Identifiers (MMSIs) of navalvessels that are included in a respective field of AIS messages, as wellas in the form of expected positions of the naval vessels.

According to another aspect of the present disclosure, a receiver fordemodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulationcomprises: normalization means for normalizing samples of a sequence ofsamples generated from the received signal, to obtain a normalizedsequence of samples, wherein an amplitude of each sample of thenormalized sequence of samples has an absolute value equal to unity;estimation means for estimating, on the basis of the normalized sequenceof samples, a time offset and a frequency offset of the received signal,and using the estimated time offset and the estimated frequency offsetfor compensating the normalized sequence of samples for the time andfrequency offsets, to obtain a compensated sequence of samples; anddecoding means for determining a sequence of symbols corresponding tothe transmitted sequence of symbols on the basis of the compensatedsequence of samples. The receiver may further comprise sampling meansfor generating the sequence of samples on the basis of the receivedsignal.

A particular advantage is achieved if the estimation means comprises: afirst low-pass filter for filtering the normalized sequence of samplesto obtain a first filtered sequence of samples; time offset estimationmeans for determining the estimate of the time offset on the basis of afirst result obtained by auto-correlating the first filtered sequence ofsamples; first frequency offset estimation means for determining a firstestimate of the frequency offset on the basis of the first result; andfirst compensation means for compensating the normalized sequence ofsamples for the frequency offset using the first estimate of thefrequency offset, to obtain a first compensated sequence of samples. Thefirst frequency offset estimation means may be configured to determinethe first estimate of the frequency offset further on the basis of theestimate of the time offset. Preferably, the estimation means furthercomprises: a second low-pass filter for filtering the first compensatedsequence of samples to obtain a second filtered sequence of samples;second frequency offset estimation means for determining a secondestimate of the frequency offset on the basis of a second resultobtained by auto-correlating the second filtered sequence of samples;second compensation means for compensating the first compensatedsequence of samples for the frequency offset using the second estimateof the frequency offset, to obtain a second compensated sequence ofsamples; and interpolation means for interpolating the secondcompensated sequence of samples on the basis of the estimate of the timeoffset to obtain the compensated sequence of samples. The secondfrequency offset estimation means may be configured to determine thesecond estimate of the frequency offset further on the basis of theestimate of the time offset.

Alternatively, the estimation means may comprise: a first low-passfilter for filtering the normalized sequence of samples to obtain afirst filtered sequence of samples; time offset estimation means fordetermining the estimate of the time offset on the basis of a firstresult obtained by auto-correlating the first filtered sequence ofsamples; and interpolation means for interpolating the normalizedsequence of samples on the basis of the estimate of the time offset toobtain an interpolated sequence of samples. The estimation means mayfurther comprise: a second low-pass filter for filtering theinterpolated sequence of samples to obtain a second filtered sequence ofsamples; down-sampling means for down-sampling the second filteredsequence of samples to obtain a first down-sampled sequence of samples;first frequency offset estimation means for determining a first estimateof the frequency offset on the basis of a second result obtained byauto-correlating the first down-sampled sequence of samples; and firstcompensation means for compensating the interpolated sequence of samplesfor the frequency offset using the first estimate of the frequencyoffset, to obtain a first compensated sequence of samples. The firstfrequency offset estimation means may be configured to determine thefirst estimate of the frequency offset further on the basis of theestimate of the time offset.

In addition to the above, the estimation means may further comprise: athird low-pass filter for filtering the first compensated sequence ofsamples to obtain a third filtered sequence of samples; second frequencyoffset estimation means for determining a second estimate of thefrequency offset on the basis of a third result obtained byauto-correlating the third filtered sequence of samples; and secondcompensation means for compensating the first compensated sequence ofsamples for the frequency offset using the second estimate of thefrequency offset, to obtain the compensated sequence of samples. Thesecond frequency offset estimation means may be configured to determinethe second estimate of the frequency offset further on the basis of theestimate of the time offset.

Preferably, the decoding means is configured to determine the sequenceof symbols such that each of the determined symbols is a symbol that hasa highest probability of being identical to the correspondingtransmitted symbol.

According to yet another aspect of the present disclosure, a receiverfor demodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulationcomprises: estimation means for estimating, on the basis of a sequenceof samples generated from the received signal, a time offset and afrequency offset of the received signal, and using the estimated timeoffset and the estimated frequency offset for compensating the sequenceof samples for the time and frequency offsets, to obtain a compensatedsequence of samples; and decoding means for determining a sequence ofsymbols corresponding to the transmitted sequence of symbols on thebasis of the compensated sequence of samples, wherein each of thedetermined symbols is a symbol that has a highest probability of beingidentical to the corresponding transmitted symbol. The receiver mayfurther comprise sampling means for generating the sequence of samplesfrom the received signal.

Preferably, the decoding means is further configured to determine, foreach determined symbol, a probability of the determined symbol beingidentical to the corresponding transmitted symbol.

It is also of advantage if the receiver further comprises: packetgenerating means for generating a packet from the determined sequence ofsymbols, checksum calculating means for calculating a checksum for thepacket, and inverting means for inverting, if the checksum indicatesthat the packet has not been decoded correctly, a pair of symbols in thepacket, wherein the two symbols of the pair of symbols are separated bya further symbol.

Preferably, the receiver further comprises means for determining a firstpair of symbols having the lowest probability of being identical to thecorresponding transmitted symbols in the packet that has been judged asnot decoded correctly, wherein the inverting means is further configuredto invert the symbols of the determined first pair of symbols.Alternatively, the receiver may comprise means for determining a firstpair of symbols having the lowest probability of being identical to thecorresponding transmitted symbols and a second pair of symbols havingthe next-to-lowest probability of being identical to the correspondingtransmitted symbols in the packet that has been judged as not decodedcorrectly, wherein the inverting means is configured to invert, notnecessarily in this order, the symbols of the first pair only, thesymbols of the second pair only, and the symbols of the first and secondpairs simultaneously, until the checksum of the resulting packetindicates that the resulting packet has been decoded correctly. As afurther alternative, the receiver may comprise means for determining,for the packet that has been judged as not decoded correctly, an errorsequence on the basis of the checksum and a pre-stored table indicatinga relationship between checksum values and error sequences, wherein theinverting means is further configured for inverting pairs of symbolsthat are located in the packet at positions indicated by the errorsequence.

The present application further suggests that the sequence of sampleshas a first ratio of samples per transmitted symbol, the receiverfurther comprises down-sampling means for down-sampling the compensatedsequence of samples to obtain a down-sampled sequence of samples, thedown-sampled sequence of samples having a second ratio of samples pertransmitted symbol lower than the first ratio of samples, and thedecoding means is configured to determine the sequence of symbolscorresponding to the transmitted sequence of symbols on the basis of thedown-sampled sequence of samples. Preferably, the first ratio is 3 ormore, and the second ratio is 1.

The receiver may further comprise: means for identifying packets ofsymbols which have been decoded correctly, and cancellation means forcancelling said correctly decoded packets from the sequence of samplesgenerated from the received signal by subtracting, from the sequence ofsamples, a reconstructed signal that has been reconstructed from saidcorrectly decoded packets to obtain an interference-cancelled sequenceof samples to be used for further demodulation processing.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates the partition of an AIS channel into threeoverlapping sub-bands according to the prior art;

FIG. 2 is a schematic representation of a receiver according to anembodiment of the present disclosure;

FIG. 3 is a flow chart illustrating a process flow for demodulating areceived signal according to an embodiment of present disclosure;

FIG. 4 is a schematic representation of a time and frequency estimatorin the receiver of FIG. 2 for estimating a time offset and a frequencyoffset of the received signal according to an embodiment of presentdisclosure;

FIG. 5 is a flow chart illustrating a process flow for estimating thetime offset and the frequency offset of the received signal in the timeand frequency estimator of FIG. 4;

FIG. 6 is a schematic representation of a time and frequency estimatorin the receiver of FIG. 2 for estimating a time offset and a frequencyoffset of the received signal according to another embodiment of presentdisclosure;

FIG. 7 is a flow chart illustrating a process flow for estimating thetime offset and the frequency offset of the received signal in the timeand frequency estimator of FIG. 6;

FIG. 8 is a schematic representation of a time and frequency estimatorin the receiver of FIG. 2 for estimating the time offset and thefrequency offset of the received signal according to another embodimentof present disclosure;

FIG. 9 is a flow chart illustrating a process flow for estimating thetime offset and the frequency offset of the received signal in the timeand frequency estimator of FIG. 8;

FIG. 10 is a schematic representation of a time and frequency estimatorin the receiver of FIG. 2 for estimating the time offset and thefrequency offset of the received signal according to another embodimentof present disclosure;

FIG. 11 is a flow chart illustrating a process flow for estimating thetime offset and the frequency offset of the received signal in the timeand frequency estimator of FIG. 10;

FIG. 12 is a flow chart for illustrating a process flow forpost-processing of a demodulated signal according to an embodiment ofthe disclosure;

FIG. 13 is a flow chart for illustrating a process flow forpost-processing of the demodulated signal according to anotherembodiment of the disclosure;

FIG. 14 is a flow chart for illustrating a process flow forpost-processing of the demodulated signal according to anotherembodiment of the disclosure;

FIG. 15 is a flow chart illustrating a process flow for performinginterference cancellation in the received signal according to anembodiment of the disclosure;

FIG. 16 is a flow chart illustrating a process flow for data-aideddecoding of a received packet according to an embodiment of thedisclosure;

FIG. 17 is a graph illustrating the performance of the presentapplication compared to the prior art; and

FIG. 18A, 18B are graphs illustrating the performance of the method withand without data-aided decoding.

DETAILED DESCRIPTION

Preferred embodiments of the present disclosure will be described in thefollowing with reference to the accompanying figures, wherein in thefigures identical objects are indicated by identical reference numbers.It is understood that the present invention shall not be limited to thedescribed embodiments, and that the described features and aspects ofthe embodiments may be modified or combined to form further embodimentsof the present disclosure.

The present disclosure relates to a method for demodulating a receivedsignal relating to a sequence of transmitted symbols that have beenmodulated by continuous phase modulation and to a receiver fordemodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulation. Invarious embodiments, the method disclosed herein is advantageouslyadopted to each zonal demodulator of the prior art receiver known fromEP 2 315 366 A1.

Signal Model and Notation

First, as a foundation of the detailed description that will bepresented below, the underlying signal model and notation will bepresented.

Following J. B. Anderson, T. Aulin, and C.-E.W. Sundberg, Digital PhaseModulation, New York: Plenum Press, 1986 (Anderson et al.), the complexenvelope of a CPM signal can be written as

$\begin{matrix}{{{s\left( {t,\alpha} \right)} = {\sqrt{\frac{2E_{S}}{T}}\exp \left\{ {j\; 2\pi \; h{\sum\limits_{n = 0}^{N - 1}\; {\alpha_{n}\mspace{14mu} {q\left( {t - {nT}} \right)}}}} \right\}}},} & \left( {{eq}.\mspace{14mu} 1.1} \right)\end{matrix}$

where E_(S) is the energy per information symbol, T is the symbolinterval, h is the modulation index, N is the number of transmittedinformation symbols, α={a_(n)}_(n=0) ^(N-1) is the information sequence,and q(t) is the phase pulse, constrained to be such that

$\begin{matrix}{{q(t)} = \left\{ \begin{matrix}{0,} & {t \leq 0} \\{\frac{1}{2},} & {t \geq {LT}}\end{matrix} \right.} & \left( {{eq}.\mspace{14mu} 1.2} \right)\end{matrix}$

L being the correlation length. Several examples of commonly used phasepulses are reported in Anderson et al.

The modulation index is usually written as h=r/p (where r and p arerelatively prime integers), and the information symbols belong to theM-ary alphabet {±1, ±3, . . . , ±(M−1)}, M being a power of two. Forthis case, it has been shown in B. E. Rimoldi, A decomposition approachto CPM, IEEE Trans. Inform. Theory, vol. 34, pp. 260-270, March 1988(Rimoldi) that the CPM signal in the generic time interval [nT, (n+1)T]is completely defined by the symbol α_(n), the correlative state ω_(n)and the phase state φ_(n). Therein, the correlative state ω_(n) is givenby

ω_(n)=(α_(n-1),α_(n-2), . . . ,α_(n-L+1)),  (eq. 1.3)

and the phase state φ_(n) can be recursively defined as

φ_(n)=[φ_(n-1) +πhα _(n-L)]_(2π),  (eq. 1.4)

where [•]_(2π) denotes the “modulo 2π” operator. In other words, we mayexpress the complex envelope of a CPM signal as

$\begin{matrix}{{{s\left( {t,\alpha} \right)} = {\sqrt{\frac{2E_{S}}{T}}{\sum\limits_{n = 0}^{N - 1}{{s_{T}\left( {{{t - {nT}};\alpha_{n}},\omega_{n}} \right)}^{j\; \varphi_{n}}}}}},} & \left( {{eq}.\mspace{14mu} 1.5} \right)\end{matrix}$

(Rimoldi decomposition) where S_(T)(t−nT; α_(n), ω_(n)) is a slice ofsignal of length T (with support in [nT, (n+1)T]) whose shape onlydepends on symbol α_(n) and correlative state ω_(n) and is independentof the considered symbol interval. For the initialization of recursionin (eq. 1.4), the following conventions are adopted:

φ₀=0,  (eq. 1.6)

α_(n)=0 ∀n<0  (eq. 1.7)

At any given time epoch n, the correlative state ω_(n) can assumeM^(L-1) different values, while the phase state φ_(n) can assume pdifferent values, so that the CPM signal can be described by means of afinite-state machine with pM^(L-1) possible values of the stateσ_(n)=(ω_(n), φ_(n)). When n is even, the p values assumed by the phasestate φ_(n) belong to the alphabet

_(e)={2πhm, m=0,1, . . . , p−1}, while, when n is odd, belong to thealphabet

_(o)={2πh,m+πh,m=0,1, . . . ,p−1} (when r is even,

_(o) and

_(e) coincide). In the following, the integer representation for thephase state and information symbols

α_(n)=2α _(n)−(M−1),  (eq. 1.8)

φ_(n) =−πh(M−1)n+2πφ _(n)  (eq. 1.9)

will be adopted, so that α _(n)ε{0,1, . . . , M−1} and φ _(n)ε{0,1, . .. , p−1}. The integer φ _(n) can be recursively updated according to

φ _(n)=[φ _(n-1)+α _(n)]_(p)  (eq. 1.10)

Following U. Mengali and M. Morelli, Decomposition of M-ary CPM signalsinto PAM waveforms, IEEE Trans. Inform. Theory, vol. 41, pp. 1265-1275,September 1995 (Mengali et al. 1995), the complex envelope of a CPMsignal (eq. 1.1) may be exactly expressed as

$\begin{matrix}{{{s\left( {t,\alpha} \right)} = {\sum\limits_{k = 0}^{F - 1}\; {\sum\limits_{n}{\alpha_{k,n}{p_{k}\left( {t - {nT}} \right)}}}}},} & \left( {{eq}.\mspace{14mu} 1.11} \right)\end{matrix}$

based on a Laurent decomposition, where F=(M−1)2^((L-1)log) ² ^(M) isthe number of linearly modulated pulses {p_(k)(t)}, and {α_(k,n)} arethe so-called pseudo-symbols (hereafter, simply referred to as symbols).The expressions of pulses {p_(k)(t)} and those of symbols {α_(k,n)} as afunction of the modulation parameters and of the information symbols{α_(n)} can be found in Mengali et al. 1995. By truncating the summationin (eq. 1.11) to the first K<F terms, the approximation

$\begin{matrix}{{s\left( {t,\alpha} \right)} \simeq {\sum\limits_{k = 0}^{K - 1}\; {\sum\limits_{n}{\alpha_{k,n}{p_{k}\left( {t - {nT}} \right)}}}}} & \left( {{eq}.\mspace{14mu} 1.12} \right)\end{matrix}$

is obtained.

Most of the signal power is concentrated in the first M−1 components,i.e., those associated with the pulses {p_(k)(t)} with 0≦k≦M−2, whichare denoted as principal components. As a consequence, a value of K=M−1may be used in (eq. 1.12) to attain a very good tradeoff betweenapproximation quality and number of signal components. A nice feature ofthe principal components is that their symbols {α_(k,n)}_(k=0) ^(M-2)can be expressed as a function of a_(n) and a_(0,n-1) only.

The GMSK modulation format described in K. Murota, K. Hirade, GMSKmodulation for digital mobile radio telephony, IEEE Trans. Commun., vol.29, pp. 1044-1050, July 1981 (Murota et al.) is a binary CPM (hence M=2,α_(n)ε{±1}, and E_(S)=E_(b), where E_(b) is the energy per informationbit) with modulation index h=1/2 and phase pulse mathematicallydescribed in Murota et al. The derivative of this phase pulse can beobtained by filtering a rectangular pulse of length T with a Gaussianfilter of proper −3 dB bandwidth B. In the case of AIS, the value of Bnormalized to the symbol rate is BT=0.4÷0.5. For an illustration of thephase pulse for the case of a unitary-amplitude GMSK signal, it isreferred to Murota et al. Although in this case the correlation lengthis in principle unlimited, L=2÷3 can be assumed, wherein simulationsconducted by the inventors show that there is no appreciable differencebetween the cases of L=3 and L=2. Preferably, L=3 is chosen in thecontext of the present disclosure.

Considering now the Laurent representation of a GMSK signal, in thiscase there is only M 1=1 principal component and (eq. 1.12) turns into

$\begin{matrix}{{s\left( {t,\alpha} \right)} = {\sum\limits_{n}{\alpha_{0,n}{{p_{0}\left( {t - {nT}} \right)}.}}}} & \left( {{eq}.\mspace{14mu} 1.13} \right)\end{matrix}$

To simplify the notation, the definitions

α_(n)=α_(0,n)

p(t)=p ₀(t).

will be used in the following.

The principal pulse p(t) is calculated according to Mengali et al. 1995,further according to which symbol α_(n) can be recursively computed as

α_(n) =jα _(n)α_(n-1)  (eq. 1.14)

and is related to the phase state φ_(n) via

α_(n-1) =e ^(jφn).

After this brief introduction of the underlying signal model andnotation relating thereto, now the implementation, architecture andoperation of an embodiment of a digital receiver will be described.

Receiver Architecture

Although in the following, for convenience in the mathematicalderivations, it will mostly be assumed that a continuous-time signal isavailable, a digital implementation of the receiver is required. Apossible way of extracting a sufficient statistic from the receivedsignal is by means of a technique disclosed in H. Meyr, M. Oerder, A.Polydoros, On sampling rate, analog prefiltering, and sufficientstatistics for digital receivers, IEEE Trans. Commun., vol. 42, pp.3208-3214, December 1994.

It will be further assumed that the useful signal component in thereceived signal is band-limited (although this is not strictly true inthe case of CPM signals, whose spectrum has an infinite support) withbandwidth lower than η/2T, where η is a proper integer. The complexenvelope of the received signal is pre-filtered using an analog low-passfilter which leaves unmodified the useful signal and has a vestigialsymmetry around η/2T. A sufficient statistic can be obtained byextracting η samples per symbol interval from the signal after theanalog pre-filter and, in addition, the condition on the vestigialsymmetry of the analog pre-filter ensures that the noise samples areindependent and identically distributed complex Gaussian randomvariables with mean zero and variance 2N₀η/T, 2N₀ being the powerspectral density (PSD) of the noise complex envelope.

Considering a reasonable power spectral density of the GMSK signal andthe maximum frequency uncertainty that can be tolerated, η=3 samples perbit interval are sufficient and, without loss of generality, will beconsidered in the following.

An implementation of a digital receiver 200 according to the presentdisclosure will now be described with reference to FIG. 2. With thisexample, the overall architecture of the receiver 200 corresponds tothat of the prior art receiver disclosed in EP 2 315 366 A1, whereinhowever the zonal demodulators 210, 230, 250 of the receiver 200 aredifferent from those of the prior art receiver.

The signal received from a VHF antenna is first processed by a front endunit 201 (sampling means) comprising an analog front end and an A/Dconverter. The resulting discrete-time signal (i.e., sequence ofsamples) is properly shifted in frequency by frequency shifting means202, 203 for each of three zonal demodulators 210, 230, 250 and eachresulting signal is sent to the respective zonal demodulator.Alternatively, instead of a parallel implementation, the same zonaldemodulator can be reused to reduce the hardware complexity. In thiscase however, obviously, the latency will increase. Output signals ofthe three zonal demodulators 210, 230, 250 are brought together in amessage parser 204 for obtaining and outputting the demodulated AISmessages.

As indicated above, in order to exploit the frequency diversityresulting from the Doppler spread, the receiver 200 consists of threezonal demodulators 210, 230, 250, each of which is specifically designedto process only one slice of the AIS channel and to achieve the targetperformance within that slice. Since the estimation range of thefrequency estimator 232 is slightly less than

${\pm \frac{0.21}{T}},$

and taking into account that due to a maximum Doppler shift of ±4 kHzand a maximum frequency uncertainty of transmit and receive oscillatorsof ±1.8 kHz, the maximum value of the frequency uncertainty is

${{{\pm 5.8}\mspace{14mu} {kHz}} = {\pm \frac{0.604}{T}}},$

it is suggested to center the zonal demodulators at the nominalfrequency, at the nominal frequency

$+ \frac{0.4}{T}$

and at the nominal

${- \frac{0.4}{T}},$

respectively. This slicing of the AIS channel is illustrated in FIG. 1.Nevertheless, while the three zonal demodulators 210, 230, 250 areconfigured to demodulate signals of different frequencies, theirunderlying architecture is very similar.

Each of the three zonal demodulators 210, 230, 250 included in thereceiver 200 is composed of three main sub-blocks that are properlyinterconnected. By way of example, in the following, the architecture ofthe zonal demodulator 230 that is fed with the un-shifted signal will bedescribed. The zonal demodulator 230 comprises a pre-detectionsynchronization unit 231, 232, a matched filter 233, 234 (down-samplingmeans), a detection unit 235 (decoding means), and a post-detectionsynchronization unit 237-243.

The pre-detection synchronization unit performs a preliminary estimationof all channel parameters that need to be compensated before detection.The accuracy of these estimates must be higher than the sensitivity ofthe detection algorithm to an uncompensated error. The pre-detectionsynchronization unit comprises a limiter 231 normalization means) and atiming and frequency estimator 232 (estimation means). The limiter 231is applied to the sequence of received samples r_(n) (complex valuedsamples) and limits the magnitude of each of the complex samples tounity. In other words, the limiter 231 generates a sequence ofnormalized samples r_(n)′ defined by r_(n)′=^(r) ^(n) /_(|r) _(n) _(|),that is the limiter 231 generates a sequence of normalized samplesr_(n)′ by dividing each sample r_(n) by its respective absolutemagnitude |r_(n)|.

The sequence of normalized samples is fed to the timing and frequencyestimator 232 which estimates a time offset (timing offset) and afrequency offset of the received signal. Therein, the time offset is anoffset relative to a given time frame and corresponds to an offset offirst bits of respective packets of the received signal with respect tothe given time frame (e.g., a time frame of the receiver provided byGPS), and the frequency offset is an offset relative to a givenfrequency and corresponds to an offset between the frequency of thereceived signal with respect to the respective carrier frequency of theAIS. For satellite-based AIS, the frequency offset is due to a Dopplershift. The timing and frequency estimator 232 works on a window of L₀symbols and uses η=3 samples per symbol. The estimates of the timeoffset and the frequency offset are used to compensate (correct) thenormalized sequence of samples for said offsets, i.e., to compensate forthe impact of these offsets by shifting the normalized sequence ofsamples in time and in frequency. As a result, the timing and frequencyestimator 232 outputs an estimate {circumflex over (τ)} of the timeoffset and a compensated (corrected) sequence of samples. More detailson the pre-detection synchronization unit will be provided below.

After frequency and timing estimation and compensation in the timing andfrequency estimator 232, the received signal is filtered using thematched filter (oversampled filter) 233, 234 which is matched to theprincipal pulse of the Laurent decomposition. One sample per symbolinterval is retained at the output of the matched filter 233, 234 whichuses the information on the estimate {circumflex over (τ)} of the timeoffset provided by the timing and frequency estimator 232 in the processof down-sampling. The sequence of samples output by the matched filter233, 234 (down-sampled sequence of samples) is fed to the detection unit235.

The detection unit 235 detects (decodes) packets corresponding to thesequence of samples input thereto. More details on the detection unit235 and its operation will be provided below.

The post-detection synchronization unit performs a fine estimation ofthe channel parameters needed for the reconstruction and cancellation ofthe detected signal (i.e., the detected packets). In general, theestimation accuracy must be greater than that of the pre-detectionsynchronization unit. The post-detection synchronization unit comprisesa frequency estimator 237, a signal reconstructor 238, a firstcompensator 239, a quadratic interpolator 240, a phase and amplitudeestimator 241, a second compensator 242, and a subtractor 243.

In the frequency estimator 237, a more refined estimate of the frequencyoffset is determined. The signal reconstruction unit 238 is in practicea discrete-time CPM modulator followed by the quadratic interpolator 240that, taking into account the timing estimate performed in thepre-detection stage, tries to align the reconstructed signal and thereceived samples. Since the discrete-time CPM modulator 238 has toproduce three samples for each couple (α_(n), φ_(n)) (the correlativestate is absent in the case of GMSK and the phase state φ_(n) takes ontwo values), it can be conveniently implemented through a look-up table.In the first compensator 239, the reconstructed signal is compensatedfor an effect of the frequency offset of the received signal on thebasis of the more refined estimate of the frequency offset before inputto the quadratic interpolator 240. In the phase and amplitude estimator241, a phase and an overall amplitude of the received signal isdetermined, and the output of the quadratic interpolator 240 iscompensated in the second compensator 242 for phase and amplitude of thereceived signal as determined by the phase and amplitude estimator 241(i.e., so as to match the phase and amplitude of the received signal).

The output of the second compensator 242 is input to the subtractor 243in which the processed reconstructed signal (i.e., the reconstructedsequence of samples) is subtracted from the received signal (i.e., thesequence of samples derived therefrom), in order to cancel correctlydecoded packets from the received signal, thereby cancellinginterference by these packets. More details on the post-detectionsynchronization unit will be provided below.

Lastly, frame synchronization is performed by computing the CRC for the128 possible positions of the start of a message. When the rightposition is found, the CRC is verified, the search is stopped and thesuccessfully decoded message is passed on to the message parser block204, which has the functions of discarding duplicated messages andpassing the successfully detected messages to the signal reconstructorof each zonal demodulator 210, 230, 250.

Next, operation of the receiver 200, i.e., a procedure for demodulatinga received signal relating to a sequence of transmitted symbols thathave been modulated by continuous phase modulation, will be describedwith reference to the flow chart of FIG. 3.

At step S301, a sequence of samples r_(n) is generated from the receivedsignal. This step is performed at the front end unit 201, which in thissense acts as sampling means.

At step S302, the samples r_(n) of the sequence of samples arenormalized, thereby obtaining a normalized sequence of samples r_(n)′.After normalization, the amplitude of each sample of the normalizedsequence of samples has an absolute value equal to unity, i.e.,|r_(n)′|=1 or r_(n)′=_(e) ^(jXn). The normalized samples r_(n)′ areobtained via r_(n)′=^(r) ^(n) /_(|r) _(n) _(|). This step is performedin the limiter 231, which in this sense acts as normalization means.

At step S303, a time offset and a frequency offset of the receivedsignal are estimated on the basis of the normalized sequence of samples.Further, at step S304, the estimated time offset and estimated frequencyoffset are used for compensating (correcting) the normalized sequence ofsamples for time and frequency offsets, thereby obtaining a compensatedsequence of samples. Both steps S303 and S304 are performed in thetiming and frequency estimator 232, which in this respect act as anestimation means.

At step S305, a sequence of symbols corresponding to the transmittedsequence of symbols is determined on the basis of the compensatedsequence of samples. This step is performed in the detection unit 235,which in this sense acts as demodulation means.

In the following, the operation in the above three units and in theprincipal components of the receiver 200 will be described in moredetail.

Pre-Detection Stage

First, the pre-detection synchronization unit and its operation will bedescribed. The aim of this first synchronization stage is to estimate,in a non-data-aided (NDA) mode (data-aided solutions do not seem to beviable because of the very low number of known symbols in thetransmitted sequence), and compensate for the frequency offset F and thetiming offset τ that affect the received signal.

The complex envelope r(t) of the received signal can be modeled by

r(t)=As(t−τ,α)exp{jθ}exp{j2πFt}+w(t),  (eq. 3.1)

where a constant amplitude A, a constant phase offset θ, and an additivewhite Gaussian noise (AWGN) process w(t) modeling the noise complexenvelope are also accounted for. It is to be noted that any interferingusers are not included in (eq. 3.1), since the interference can beneglected in the present stage of the receiver design. On the otherhand, the impact of the interferers on the receiver performance can beevaluated using extensive computer simulations. It is further pointedout that the pre-detection synchronization stage does not attempt torecover the phase offset θ of the received signal, since the detectionunit 235 described below can tolerate the presence of this offset.

As has been found by the inventors, in the presence of interference aperformance improvement of the receiver can be obtained when samplesr_(n), after cancellation of the previously detected signal, arenormalized to unit amplitude. This normalization is performed by thelimiter 231 which is applied to the received samples. The advantage ofsuch a transformation in the presence of interference is found to bemuch higher than the performance degradation that might result in theabsence of interference.

In the context of the present disclosure, four different alternativeimplementations for the timing and frequency estimator 232 which iscomprised by the pre-detection synchronization unit are proposed. Thealternative implementations are found to have similar performance—unlessa long sequence of zeros is present in the data field, producing a largebias in the performance of some of them—but have different complexity.For three of these implementations, two instances of frequencyestimation are required to avoid a bias problem of the frequencyestimation that would reduce the estimation range, while a secondinstance of the frequency estimation is not necessary for one of theimplementations.

According to all four implementations, the estimate of the time offsetand the estimate of the frequency offset are determined using afeed-forward algorithm that involves performing an auto-correlation of asequence of samples input to the algorithm. In this algorithm, thenormalized sequence of samples output by the limiter 231 is filteredusing a low-pass filter to obtain a filtered sequence of samples, theestimate of the time offset is determined on the basis of a first resultobtained by auto-correlating the filtered sequence of samples, and theestimate of the frequency offset is determined on the basis of a secondresult obtained by auto-correlating the filtered sequence of samples ora first sequence of samples derived from the normalized sequence ofsamples. Then, the normalized sequence of samples or a second sequenceof samples derived from the normalized sequence of samples isinterpolated on the basis of the estimate of the time offset, and thenormalized sequence of samples or a third sequence of samples derivedfrom the normalized sequence of samples is compensated for the frequencyoffset using the estimate of the frequency offset, to obtain thecompensated sequence of samples. Determination of the estimate of thefrequency offset may be further based on the estimate of the timeoffset.

Next, the four implementations 400, 600, 800, 1000 of the timing andfrequency estimator 232 and their respective operations will bedescribed in more detail with reference to FIGS. 4 to 10.

A first implementation 400 of the timing and frequency estimator 232 isillustrated in the block diagram of FIG. 4. Operation of the firstimplementation 400 is illustrated in the flow chart of FIG. 5.

According to the first implementation 400, the timing and frequencyestimator comprises a first low-pass filter 401, a timing estimator 402(time offset estimation means), a first frequency estimator 403 (firstfrequency estimation means), a second low-pass filter 405, a secondfrequency estimator 406 (second frequency estimation means), first andsecond compensators 404, 407 (first and second compensation means), andan interpolator 408 (interpolation means) which is a quadraticinterpolator.

The first implementation 400 is roughly based on the synchronizationalgorithm (Mengali-Morelli algorithm) proposed in M. Morelli and U.Mengali, Joint frequency and timing recovery for MSK-type modulation,IEEE Trans. Commun., vol. 47, pp. 938-946, June 1999. After the limiter231, the received and normalized samples {r_(n)} (for reasons ofsimplicity of notation, the prime indicating the normalized samples willbe dropped in the following) are filtered using the first low-passfilter 401, implemented through a finite impulse response (FIR) filterwith a limited number of coefficients, having bandwidth B_(LP) which isa design parameter of the synchronization algorithm. Let {z_(n)} denotethe filtered samples (first filtered sequence of samples), indexed fromn=0 to n=ηL₀−1, which correspond to L₀ signaling intervals (L₀=128 isthe case of interest for the AIS scenario when packets of length 224bits are considered, as those in the AIS 1 and 2 channels; when shorterpackets of length 152 bits, as those in the AIS 3 channels, areconsidered, it can be assumed that L₀=88).

Next, the following coefficients {circumflex over (R)}_(m)(i) arecomputed as a first result by auto-correlating the first filteredsequence of samples z_(n) via

$\begin{matrix}{{{{\hat{R}}_{m}(i)} = {\frac{1}{L_{0} - m}{\sum\limits_{n = m}^{L_{0} - 1}\; \left\lbrack {z_{{n\; \eta} + i}z_{{{({n - m})}\eta} + i}^{*}} \right\rbrack^{2}}}},} & \left( {{eq}.\mspace{14mu} 3.2} \right)\end{matrix}$

for iε{0,1, . . . , n−1} and mε{1,2, . . . , M_(α1)}, M_(α1) being adesign parameter of the synchronization algorithm (preferably, a valueof M_(α1)=20 is selected). The estimate {circumflex over (τ)} of thetime offset is then computed in the timing estimator 402 as

$\begin{matrix}{{\hat{\tau} = {{- \frac{T}{2\pi}}\arg \left\{ {\sum\limits_{i = 0}^{\eta - 1}\; {\sum\limits_{m = 1}^{M_{a\; 1}}\; {A_{1}(m)}}} \middle| {{\hat{R}}_{m}(i)} \middle| {\exp \left\{ {{- j}\; 2\pi \; i\text{/}\eta} \right\}} \right\}}},} & \left( {{eq}.\mspace{14mu} 3.3} \right)\end{matrix}$

where the terms {A₁(m)} are real coefficients that can be pre-computedoff-line, based only on the modulation format.

Finally, a first estimate {circumflex over (F)} of the frequency offsetis computed in the first frequency estimator 403 as

$\begin{matrix}{{\hat{F} = {\frac{1}{4\pi \; M_{a\; 1}T}{\sum\limits_{m = 1}^{M_{a\; 1}}\; {\arg \left\{ {\mu_{m}{{\hat{R}}_{m}\left( i_{m} \right)}{{\hat{R}}_{m - 1}^{*}\left( i_{m - 1} \right)}} \right\}}}}},} & \left( {{eq}.\mspace{14mu} 3.4} \right)\end{matrix}$

where the terms {μ_(m)} are again real coefficients that can bepre-computed off-line, based only on the modulation format. For themodulation format of interest in the context of the present disclosure,μ_(m)=−1 is obtained for all values of m. In (eq. 3.4), {circumflex over(R)}₀(∩) is conventionally equal to one, while the terms {i_(m)} arecomputed according to the Mengali-Morelli algorithm, or by simplyquantizing the value of {circumflex over (τ)}/T to the closest integer(modulo η). Thus, the first estimate {circumflex over (F)} of thefrequency offset is determined on the basis of the first result. In moredetail, the first estimate {circumflex over (F)} of the frequency offsetis determined on the basis of the first result and the estimate{circumflex over (τ)} of the time offset.

Using the first estimate r of the frequency offset, the sequence ofreceived and normalized samples {r_(n)′} is compensated for the impactof the frequency offset of the received signal in the first compensator404. Thereby, a first compensated sequence of samples is generated. Inthe frequency compensation, starting from the normalized samples r_(n)′,samples r_(n)″=r_(n)′e^(−j2π{circumflex over (F)}T) ^(c) are defined,where T_(c) is the sampling interval

$\left( {{T_{c} = \frac{T}{\eta}},{i.e.},{T_{c} = \frac{T}{3}}} \right.$

when three samples per symbol are used).

After a first frequency estimation and compensation, frequencyestimation is performed again by using the same algorithm and a furthercompensation is performed. That is, the compensated samples are filteredusing the second low-pass filter 405, implemented in the same manner asthe first low-pass filter 401, to generate a second filtered sequence ofsamples. Then, coefficients {circumflex over (R)}_(m)(i) are computed asa second result by auto-correlating the second filtered sequence ofsamples via (eq. 3.2). A second estimate {circumflex over (F)}′ of thefrequency offset is computed in the second frequency estimator 406 via(eq. 3.4), i.e., on the basis of the second result. In more detail, thesecond estimate {circumflex over (F)}′ of the frequency offset isdetermined on the basis of the second result and the estimate{circumflex over (τ)} of the time offset. Subsequently, using the secondestimate {circumflex over (F)}′ of the frequency offset, the firstcompensated sequence of samples is again compensated for the impact ofthe frequency offset of the received signal, this time in the secondcompensator 407. Thereby, a second compensated sequence of samples isgenerated.

Finally, quadratic interpolation of the second compensated sequence ofsamples is performed in the interpolator 408 based on the derivedestimate {circumflex over (τ)} of the time offset in order to correctfor the time offset. Thereby, the compensated sequence of samples isobtained that is later subjected to detection.

In the above, it is to be understood that the components of the timingand frequency estimator according to the first implementation 400 can beimplemented either or both in hardware or software. Correspondingstatements hold true also for the second to fourth implementations 600,800, 1000 of the timing and frequency estimator that will be describedbelow.

The operation of the first implementation 400 of the timing andfrequency estimator described above is summarized in the flow chart ofFIG. 5. At step S501, the normalized sequence of samples output by thelimiter 231 is filtered using the first low-pass filter 401, therebyobtaining the first filtered sequence of samples.

At step S502, the estimate {circumflex over (τ)} of the time offset isdetermined on the basis of a first result obtained by auto-correlatingthe first filtered sequence of samples. The first result corresponds tothe coefficients {circumflex over (R)}_(m)(i) that are computedaccording to (eq. 3.2), using the filtered samples of the first filteredsequence of samples as an input. This step is performed in the timingestimator 402.

At step S503, the first estimate {circumflex over (F)} of the frequencyoffset is determined on the basis of the first result. In more detail,the first estimate {circumflex over (F)} of the frequency offset isdetermined on the basis of the estimate {circumflex over (τ)} of thetime offset and the first result. This step is performed in the firstfrequency estimator 403.

Using the first estimate {circumflex over (F)} of the frequency offset,at step S504 the normalized sequence of samples is compensated for thefrequency offset of the received signal, thereby obtaining the firstcompensated sequence of samples. This step is performed in the firstcompensator 404.

Then, at step S505, the first compensated sequence of samples isfiltered using the second low-pass filter 405, thereby obtaining thesecond filtered sequence of samples.

At step S506, the second estimate {circumflex over (F)}′ of thefrequency offset is determined on the basis of a second result obtainedby auto-correlating the second filtered sequence of samples. In moredetail, the second estimate {circumflex over (F)}′ of the frequencyoffset is determined on the basis of the estimate {circumflex over (τ)}of the time offset and the second result. The second result correspondsto the coefficients {circumflex over (R)}_(m)(i) that are computedaccording to (eq. 3.2), using the filtered samples of the secondfiltered sequence of samples as an input. This step is performed in thesecond frequency estimator 406.

Using the second estimate {circumflex over (F)}′ of the frequencyoffset, at step S506 the first compensated sequence of samples iscompensated again for the frequency offset of the received signal,thereby obtaining the second compensated sequence of samples. This stepis performed in the second compensator 407.

Lastly, at step S508 the second compensated sequence of samples isquadratically interpolated on the basis of the estimate {circumflex over(τ)} of the time offset in order to correct for the time offset.Thereby, the compensated sequence of samples is obtained. This step isperformed in the interpolator 408.

With respect to the timing and frequency estimation in the prior artreceiver disclosed by EP 2 315 366 A1, there are major differences thatallow for an improvement of the estimation performance. First of all,the prior art algorithm operates on the samples before the limiter,employs 10 auto-correlation terms instead of 20 used in the presentdisclosure, and finally performs only one instance of frequencyestimation.

A second implementation 600 of the timing and frequency estimator isillustrated in the block diagram of FIG. 6. Operation of the secondimplementation 600 is illustrated in the flow chart of FIG. 7.

According to the second implementation 600, the timing and frequencyestimator comprises a first low-pass filter 601, a timing estimator 602(time offset estimation means), an interpolator 603 (interpolationmeans) which is a quadratic interpolator, a second low-pass filter 604,a down-sampler 605 (down-sampling means), a first frequency estimator606 (first frequency offset estimation means), a third low-pass filter608, a second frequency estimator 609 (second frequency offsetestimation means), and first and second compensators 607, 610 (first andsecond compensation means).

According to the second implementation 600 of the timing and frequencyestimator, timing is estimated first by using the Mengali-Morellialgorithm, i.e., via (eq. 3.2) and (eq. 3.3), as in the firstimplementation 400, but now adopting only the even auto-correlationterms. That is, in (eq. 3.3) only terms with m even are taken intoaccount. The normalized samples output by the limiter 231 areinterpolated and filtered again, and then used to perform a coarsefrequency estimation by using the algorithm (Mehlan-Chen-Meyr algorithm)proposed in R. Mehlan, Y.-E. Chen, H. Meyr, A fully digital feedforwardMSK demodulator with joint frequency offset and symbol timing estimationfor burst mode mobile radio, IEEE Trans. Veh. Tech., vol. 42, pp.434-443, November 1993. In the Mehlan-Chen-Meyr algorithm the estimate rof the frequency offset is obtained as

$\begin{matrix}{{\hat{F} = {\frac{1}{4\pi \; T}\arg \left\{ {\sum\limits_{n = 1}^{L_{0}}\; {y_{n}^{2}\left( y_{n - 1}^{2} \right)}^{*}} \right\}}},} & \left( {{eq}.\mspace{14mu} 3.5} \right)\end{matrix}$

where {y_(n)} are the samples after limiter, interpolation, filteringand down-sampling by a factor and L₀ is still equal to 128. Then, a finefrequency estimation is performed by still using the Mengali-Morellialgorithm (see (eq. 3.4)) but adopting only the even auto-correlationterms.

In more detail, the received and normalized samples {r_(n)} are filteredusing the first low-pass filter 601, thereby generating a first filteredsequence of samples {z_(n)}. For details on the first low-pass filter601, and also on the second and third low-pass filters 604, 608 it isreferred to the above description of the first implementation 400. Next,the coefficients {circumflex over (R)}_(m)(i) are computed as a firstresult by auto-correlating the first filtered sequence of samples z_(n)via (eq. 3.2), and the estimate {circumflex over (τ)} of the time offsetis computed in the timing estimator 602 via (eq. 3.3) on the basis ofthe first result. Thus, the timing estimator 602 is identical inoperation to the timing estimator 402 according to the firstimplementation 400.

Next, quadratic interpolation of the received and normalized samples{r_(n)} is performed in the interpolator 603 based on the derivedestimate {circumflex over (τ)} of the time offset in order to correctfor the time offset. Thereby, am interpolated sequence of samples isobtained.

The interpolated sequence of samples is filtered using the secondlow-pass filter 604, implemented in the same manner as the firstlow-pass filter 601, thereby generating a second filtered sequence ofsamples. The second filtered sequence of samples is down-sampled in thedown-sampler 605 from η=3 samples per bit interval to η=1 samples perbit interval, thereby obtaining a first down-sampled sequence ofsamples. In this process, the (real) estimate of the time offset τ isexpressed as τ=iT_(c)+α, where α<T_(c) is used for the interpolation.Therein, i is a running index and identifies the samples that have to bekept when performing the down-sampling.

Then, a (coarse) first estimate {circumflex over (F)} of the frequencyoffset is determined in the first frequency estimator 606 byauto-correlating the first down-sampled sequence of samples via (eq.3.5), i.e., by using the Mehlan-Chen-Meyr algorithm. Here, it can besaid that the first estimate {circumflex over (F)} of the frequencyoffset is determined on the basis of a second result obtained byauto-correlating the first down-sampled sequence of samples, the firstresult corresponding to the sum in (eq. 3.5) using the samples of thefirst down-sampled sequence of samples as an input.

Using the first estimate {circumflex over (F)} of the frequency offset,the interpolated sequence of samples is compensated for the impact ofthe frequency offset of the received signal in the first compensator607. Thereby, a first compensated sequence of samples is generated.

After the first frequency estimation and compensation, frequencyestimation is performed again by using the Mengali-Morelli algorithm anda further compensation is performed. That is, the first compensatedsequence of samples is filtered using the third low-pass filter 608,implemented in the same manner as the first low-pass filter 601, togenerate a third filtered sequence of samples. Then, coefficients{circumflex over (R)}_(m)(i) are computed as a third result byauto-correlating the third filtered sequence of samples via (eq. 3.2). A(fine) second estimate {circumflex over (F)}′ of the frequency offset iscomputed in the second frequency estimator 609 via (eq. 3.4), i.e., onthe basis of the third result. In more detail, the second estimate{circumflex over (F)}′ of the frequency offset is determined on thebasis of the third result and the estimate {circumflex over (τ)} of thetime offset. Thus, the second frequency estimator 609 is identical inoperation to the first and second frequency estimators 404, 406according to the first implementation 400. Subsequently, using thesecond estimate {circumflex over (F)}′ of the frequency offset, thefirst compensated sequence of samples is again compensated for theimpact of the frequency offset of the received signal, this time in thesecond compensator 607. Thereby, the compensated sequence of samples isobtained that is later subjected to detection.

The operation of the second implementation 600 of the timing andfrequency estimator described above is summarized in the flow chart ofFIG. 7. At step S701, the normalized sequence of samples output by thelimiter 231 is filtered using the first low-pass filter 601, therebyobtaining the first filtered sequence of samples.

At step S702, the estimate {circumflex over (τ)} of the time offset isestimated on the basis of a first result obtained by auto-correlatingthe first filtered sequence of samples. The first result corresponds tothe coefficients {circumflex over (R)}_(m)(i) that are computedaccording to (eq. 3.2), using the filtered samples of the first filteredsequence of samples as an input and adopting only the evenauto-correlation terms. This step is performed in the timing estimator602.

At step S703, the normalized sequence of samples is interpolated on thebasis of the estimate {circumflex over (τ)} of the time offset in orderto correct for the time offset, thereby obtaining the interpolatedsequence of samples. This step is performed in the interpolator 603.

At step S704, the interpolated sequence of samples is filtered using thesecond low-pass filter 604, thereby obtaining the second filteredsequence of samples.

At step S705, the second filtered sequence of samples is down-sampled toobtain the first down-sampled sequence of samples. Here, down-samplingis performed from η=3 to η=1. If an initial value for η different from 3is chosen, down-sampling by the initial value of η is performed, so thatafter down-sampling, η=1 is obtained. This step is performed in thedown-sampler 605.

Then, at step S706, the first estimate of the frequency offset isdetermined on the basis of a second result obtained by auto-correlatingthe first down-sampled sequence of samples. The second resultcorresponds to the sum in (eq. 3.5), using the first down-sampledsequence of samples as an input. This step is performed in the firstfrequency estimator 606.

Using the first estimate {circumflex over (F)} of the frequency offset,at step S707 the interpolated sequence of samples is compensated for thefrequency offset of the received signal, thereby obtaining the firstcompensated sequence of samples. This step is performed in the firstcompensator 607.

At step S708, the first compensated sequence of samples is filteredusing the third low-pass filter 608, thereby obtaining the thirdfiltered sequence of samples.

At step S709, the second estimate {circumflex over (F)}′ of thefrequency offset is determined on the basis of a third result obtainedby auto-correlating the third filtered sequence of samples. In moredetail, the second estimate {circumflex over (F)}′ of the frequencyoffset is determined on the basis of the estimate {circumflex over (τ)}of the time offset and the third result. The third result corresponds tothe coefficients R_(m)(i) that are computed according to (eq. 3.2),using the filtered samples of the third filtered sequence of samples asan input and adopting only the even auto-correlation terms. This step isperformed in the second frequency estimator 609.

Using the second estimate {circumflex over (F)}′ of the frequencyoffset, at step S710 the first compensated sequence of samples iscompensated again for the frequency offset of the received signal,thereby obtaining the compensated sequence of samples.

As becomes apparent from the above description of the secondimplementation 600, the auto-correlation algorithm that is applied tothe first filtered sequence of samples to obtain the first result (i.e.,the Mengali-Morelli algorithm) is different from the auto-correlationalgorithm that is applied to the first down-sampled sequence of samplesto obtain the second result (i.e., the Mehlan-Chen-Meyr algorithm). Onthe other hand, the auto-correlation algorithm that is applied to thefirst filtered sequence of samples to obtain the first result (i.e., theMengali-Morelli algorithm) is also applied to the third filteredsequence of samples to obtain the third result.

A third implementation 800 of the timing and frequency estimator isillustrated in the block diagram of FIG. 8. Operation of the thirdimplementation 800 is illustrated in the flow chart of FIG. 9.

According to the third implementation 800, the timing and frequencyestimator comprises a first low-pass filter 801, a timing estimator 802(time offset estimation means), an interpolator 803 (interpolationmeans) which is a quadratic interpolator, a second low-pass filter 804,a down-sampler 805 (down-sampling means), a (first) frequency estimator806 (first frequency offset estimation means), and a (first) compensator807 (first compensation means).

According to the third implementation 800 of the timing and frequencyestimator, timing is estimated first by using the Mengali-Morellialgorithm, i.e., via (eq. 3.2) and (eq. 3.3), as in the firstimplementation 400, but now adopting only the even auto-correlationterms. The normalized samples output by the limiter 231 are theninterpolated, filtered again, and down-sampled, obtaining samples{y_(n)}. These latter samples are then employed for frequency estimationby using the algorithm (DA Mengali-Morelli algorithm) described in U.Mengali, M. Morelli, Data-aided frequency estimation for burst digitaltransmission, IEEE Trans. Commun., vol. 45, pp. 23-25, January 1997.

According to the DA Mengali-Morelli algorithm, first the followingcoefficients are computed

$\begin{matrix}{{R(m)} = {\frac{1}{L_{0} - m}{\sum\limits_{n = m}^{L_{0} - 1}\; {v_{n}v_{n - m}^{*}}}}} & \left( {{eq}.\mspace{14mu} 3.6} \right)\end{matrix}$

for mε{1,2, . . . , M_(α3)}, where L₀=128, M_(α3) is a design parameternot greater than L₀/2 (preferably, M_(α3)=L₀/2 is selected), andv_(n)=(−1)^(n)y_(n) ². Then, the estimate {circumflex over (F)} of thefrequency offset can be expressed as

$\begin{matrix}{{\hat{F} = {\frac{1}{4\pi \; M_{a\; 3}T}{\sum\limits_{m = 1}^{M_{a\; 3}}\; {{w(m)}\left\lbrack {{\arg \left\{ {R(m)} \right\}} - {\arg \left\{ {R\left( {m - 1} \right)} \right\}}} \right\rbrack}_{2\pi}}}}{where}} & \left( {{eq}.\mspace{14mu} 3.7} \right) \\{{w(m)} = {\frac{3\left\lbrack {{\left( {L_{0} - m} \right)\left( {L_{0} - m + 1} \right)} - {M_{a\; 3}\left( {L_{0} - M_{a\; 3}} \right)}} \right\rbrack}{M_{a\; 3}\left( {{4M_{a\; 3}^{2}} - {6M_{a\; 3}L_{0}} + {3L_{0}^{2}} - 1} \right)}.}} & \left( {{eq}.\mspace{14mu} 3.8} \right)\end{matrix}$

It is to be noted that according to the third implementation 800 only asingle instance of frequency estimation is performed.

In more detail, the received and normalized samples {r_(n)} are filteredusing the first low-pass filter 801, thereby generating a first filteredsequence of samples {z_(n)}. For details on the first low-pass filter801, and also on the second low-pass filter 804 it is referred to theabove description of the first implementation 400. Next, thecoefficients {circumflex over (R)}_(m)(i) are computed as a first resultby auto-correlating the first filtered sequence of samples z_(n) via(eq. 3.2), and the estimate {circumflex over (τ)} of the time offset iscomputed in the timing estimator 802 via (eq. 3.3) on the basis of thefirst result, but adopting only the even auto-correlation terms. Thus,the timing estimator 802 is identical in operation to the timingestimator 402 according to the first implementation 400.

Next, quadratic interpolation of the received and normalized samples{r_(n)} is performed in the interpolator 803 based on the derivedestimate {circumflex over (τ)} of the time offset in order to correctfor the time offset. Thereby, am interpolated sequence of samples isobtained.

The interpolated sequence of samples is filtered using the secondlow-pass filter 804, implemented in the same manner as the firstlow-pass filter 801, thereby generating a second filtered sequence ofsamples. The second filtered sequence of samples is down-sampled in thedown-sampler 805 from η=3 samples per bit interval to η=1 samples perbit interval, thereby obtaining a first down-sampled sequence ofsamples. Then, an estimate r of the frequency offset is determined inthe frequency estimator 806 by auto-correlating the first down-sampledsequence of samples via (eq. 3.6), i.e., by using the DA Mengali-Morellialgorithm. Here, it can be said that the estimate {circumflex over (F)}of the frequency offset is determined on the basis of a second resultobtained by auto-correlating the first down-sampled sequence of samples,the first result corresponding to the coefficients R(m) calculated via(eq. 3.6), using the samples of the first down-sampled sequence ofsamples as an input.

Using the estimate {circumflex over (F)} of the frequency offset, theinterpolated sequence of samples is compensated for the impact of thefrequency offset of the received signal in the compensator 807. Thereby,the compensated sequence of samples is generated that is later subjectedto detection.

The operation of the third implementation 800 of the timing andfrequency estimator described above is summarized in the flow chart ofFIG. 9. At step S901, the normalized sequence of samples output by thelimiter 231 is filtered using the first low-pass filter 801, therebyobtaining the first filtered sequence of samples.

At step S902, the estimate {circumflex over (τ)} of the time offset isestimated on the basis of a first result obtained by auto-correlatingthe first filtered sequence of samples. The first result corresponds tothe coefficients {circumflex over (R)}_(m)(i) that are computedaccording to (eq. 3.2), using the filtered samples of the first filteredsequence of samples as an input and adopting only the evenauto-correlation terms. This step is performed in the timing estimator802.

At step S903, the normalized sequence of samples is quadraticallyinterpolated on the basis of the estimate τ of the time offset in orderto correct for the time offset, thereby obtaining the interpolatedsequence of samples. This step is performed in the interpolator 803.

At step S904, the interpolated sequence of samples is filtered using thesecond low-pass filter 804, thereby obtaining the second filteredsequence of samples.

At step S905, the second filtered sequence of samples is down-sampled toobtain the first down-sampled sequence of samples. Here, down-samplingis performed from η=3 to η=1. If an initial value for η different from 3is chosen, down-sampling by the initial value of η is performed, so thatafter down-sampling, η=1 is obtained. This step is performed in thedown-sampler 805.

Then, at step S906, the estimate {circumflex over (F)} of the frequencyoffset is determined on the basis of a second result obtained byauto-correlating the first down-sampled sequence of samples. The secondresult corresponds to the coefficients R(m) that are computed accordingto (eq. 3.6), using the samples of the down-sampled sequence of samplesas an input. This step is performed in the frequency estimator 806.

Using the estimate {circumflex over (F)} of the frequency offset, atstep S907 the interpolated sequence of samples is compensated for thefrequency offset of the received signal, thereby obtaining thecompensated sequence of samples. This step is performed in thecompensator 807.

It is to be noted that steps S901 to S906 correspond to steps S701 toS706, with the exception that in step S906 the DA Mengali-Morellialgorithm instead of the Mehlan-Chen-Meyr algorithm is employed fordetermining the estimate F of the frequency offset.

As becomes apparent from the above description of the thirdimplementation 800, the auto-correlation algorithm that is applied tothe first filtered sequence of samples to obtain the first result (i.e.,the Mengali-Morelli algorithm) is different from the auto-correlationalgorithm that is applied to the first down-sampled sequence of samplesto obtain the second result (i.e., the DA Mengali-Morelli algorithm).

A fourth implementation 1000 of the timing and frequency estimator isillustrated in the block diagram of FIG. 10.

According to the fourth implementation 1000, the timing and frequencyestimator comprises a first low-pass filter 1001, a timing estimator1002 (time offset estimation means), an interpolator 1003 (interpolationmeans) which is a quadratic interpolator, a second low-pass filter 1004,a down-sampler 1005 (down-sampling means), a first frequency estimator1006 (first frequency offset estimation means), a third low-pass filter1008, a second frequency estimator 1009 (second frequency offsetestimation means), and first and second compensators 1007, 1010 (firstand second compensation means).

According to the fourth implementation 1000 of the timing and frequencyestimator, the same steps as described in connection with the thirdimplementation 800 are executed, followed by a fine frequency estimationperformed by still using the Mengali-Morelli algorithm, i.e., via (eq.3.2) and (eq. 3.3), as in the first implementation 400, but now adoptingonly the even auto-correlation terms.

In more detail, the received and normalized samples {r_(n)} are filteredusing the first low-pass filter 1001, thereby generating a firstfiltered sequence of samples {z_(n)}. For details on the first low-passfilter 1001, and also on the second and third low-pass filters 1004,1008 it is referred to the above description of the first implementation400. Next, the coefficients {circumflex over (R)}_(m)(i) are computed asa first result by auto-correlating the first filtered sequence ofsamples z_(n) via (eq. 3.2), and the estimate {circumflex over (τ)} ofthe time offset is computed in the timing estimator 1002 via (eq. 3.3)on the basis of the first result. Thus, the timing estimator 1002 isidentical in operation to the timing estimator 402 according to thefirst implementation 400.

Next, quadratic interpolation of the received and normalized samples{r_(n)} is performed in the interpolator 1003 based on the derivedestimate {circumflex over (τ)} of the time offset in order to correctfor the time offset. Thereby, am interpolated sequence of samples isobtained.

The interpolated sequence of samples is filtered using the secondlow-pass filter 1004, implemented in the same manner as the firstlow-pass filter 1001, thereby generating a second filtered sequence ofsamples. The second filtered sequence of samples is down-sampled in thedown-sampler 1005 from η=3 samples per bit interval to η=1 samples perbit interval, thereby obtaining a first down-sampled sequence ofsamples.

Then, a first estimate {circumflex over (F)} of the frequency offset isdetermined in the first frequency estimator 1006 by auto-correlating thefirst down-sampled sequence of samples via (eq. 3.6), i.e., by using theDA Mengali-Morelli algorithm. Here, it can be said that the estimate{circumflex over (F)} of the frequency offset is determined on the basisof a second result obtained by auto-correlating the first down-sampledsequence of samples, the second result corresponding to the coefficientsR(m) calculated via (eq. 3.6), using the samples of the firstdown-sampled sequence of samples as an input.

Using the first estimate {circumflex over (F)} of the frequency offset,the interpolated sequence of samples is compensated for the impact ofthe frequency offset of the received signal in the first compensator1007. Thereby, a first compensated sequence of samples is generated.

After the first frequency estimation and compensation, frequencyestimation is performed again by using the Mengali-Morelli algorithm anda further compensation is performed. That is, the first compensatedsequence of samples is filtered using the third low-pass filter 1008,implemented in the same manner as the first low-pass filter 1001, togenerate a third filtered sequence of samples. Then, coefficients{circumflex over (R)}_(m)(i) are computed as a third result byauto-correlating the third filtered sequence of samples via (eq. 3.2). A(fine) second estimate {circumflex over (F)}′ of the frequency offset iscomputed in the second frequency estimator 1009 via (eq. 3.4), i.e., onthe basis of the third result. In more detail, the second estimate{circumflex over (F)}′ of the frequency offset is determined on thebasis of the third result and the estimate {circumflex over (τ)} of thetime offset. Thus, the second frequency estimator 1009 is identical inoperation to the first and second frequency estimators 404, 406according to the first implementation 400.

Subsequently, using the second estimate {circumflex over (F)}′ of thefrequency offset, the first compensated sequence of samples is againcompensated for the impact of the frequency offset of the receivedsignal, this time in the second compensator 1007. Thereby, thecompensated sequence of samples is obtained that is later subjected todetection.

The operation of the fourth implementation 1000 of the timing andfrequency estimator corresponds to the operation of the secondimplementation 600 of the timing and frequency estimator illustrated inthe flow chart of FIG. 7, with the exception that in step S706 of FIG. 7now the DA Mengali-Morelli algorithm instead of the Mehlan-Chen-Meyralgorithm is employed for determining the first estimate {circumflexover (F)} of the frequency offset. Thus, instead of S706, the operationof the fourth implementation 1000 of the timing and frequency estimatorcomprises a step S706′ in which the first estimate {circumflex over (F)}of the frequency offset is determined on the basis of a second resultobtained by auto-correlating the first down-sampled sequence of samples,wherein the second result corresponds to the coefficients R(m)calculated via (eq. 3.6), using the samples of the first down-sampledsequence of samples as an input.

As becomes apparent from the above description of the fourthimplementation 1000, the auto-correlation algorithm that is applied tothe first filtered sequence of samples to obtain the first result (i.e.,the Mengali-Morelli algorithm) is different from the auto-correlationalgorithm that is applied to the first down-sampled sequence of samplesto obtain the second result (i.e., the DA Mengali-Morelli algorithm). Onthe other hand, the auto-correlation algorithm that is applied to thefirst filtered sequence of samples to obtain the first result (i.e., theMengali-Morelli algorithm) is also applied to the third filteredsequence of samples to obtain the third result.

Through computer simulations it has been verified by the inventors thatthe fourth implementation 1000 outperforms the first to thirdimplementations 400, 600, 800 both in presence and in absence of longsequences of zeros in the data field of respective AIS messages.

In the above, it has been assumed that the receiver 200 comprises thelimiter 231 and that a normalized sequence of samples that is output bythe limiter 231 is input to the timing and frequency estimator 232.However, if desired, the limiter may also be omitted, thereby decreasingcomplexity of the receiver 200, however at the cost of degradation ofreceiver performance in the presence of long sequences of zeros in thedata field of respective AIS messages and/or heavy interference betweenmessages. The first to fourth implementations 400, 600, 800, 1000 of thetiming and frequency estimator as discussed above are also applicable toa receiver 200 not comprising the limiter 231, in which case thesequence of samples {r_(n)} is fed to the timing and frequency estimator232 without prior normalization. In the above description of the firstto fourth implementations 400, 600, 800, 1000 of the timing andfrequency estimator, the normalized sequence of samples output by thelimiter 231 would thus have to be replaced by the sequence of samplesgenerated from the received signal.

Detection Stage

Next, the detection unit 235 and its operation will be described. Animportant difference of the detection unit 235 with respect to thedetection unit in the prior art receiver disclosed in EP 2 315 366 A1 isthat instead of Viterbi-based detection now a soft-input soft-output(SISO) algorithm is employed.

In a preferred embodiment of the disclosure, the symbols correspond tobits, so that each symbol may take values +1 or −1 (or equivalently, 1and 0). The following description of the algorithm employed by thedetection unit 235 is given for this particular case. However, thepresent disclosure shall not be construed as being limited to thisparticular case.

For the selected detection algorithm, the knowledge of the phase shiftintroduced by the channel is not necessary since the detection algorithmperforms an implicit phase estimation. This algorithm is based on theLaurent decomposition (eq. 1.13) and was originally proposed in A.Barbieri, G. Colavolpe, Simplified soft-output detection of CPM signalsover coherent and phase noise channels, IEEE Trans. Wireless Commun.,vol. 6, pp. 2486-2496, July 2007.

The received signal (i.e., the sequence of samples generated therefrom),after frequency and timing estimation and compensation, is filteredusing the matched filter (oversampled filter) 233, 234 which is matchedto the principal pulse of the Laurent decomposition. One sample persymbol interval is retained at the output of the matched filter 233, 234using the information provided by the timing synchronizer, i.e., theestimate {circumflex over (τ)} of the time offset. In the following,x_(n) will denote a sample at discrete-time n. The channel phase isquantized to the Q values of the alphabet

${\Psi = \left\{ {0,\frac{2\pi}{Q},\ldots,{\frac{2\pi}{Q}\left( {Q - 1} \right)}} \right\}},$

being a design parameter to trade performance against complexity. Thechannel phase probability density function (PDF) becomes a probabilitymass function (PMF) and P_(f,n)(ψ_(n)) and P_(b,n)(ψ_(n)) will be usedto denote the estimates of the channel phase PMF in the forward andbackward recursion, respectively. The expression of the forwardrecursion—the backward recursion proceeds similarly—is given by

$\begin{matrix}{{{P_{f,n}\left( \psi_{n} \right)} = {{H_{n}\left( \psi_{n} \right)}\left\lbrack {{\left( {1 - P_{\Delta}} \right)P\left\{ {\alpha_{n} = {- 1}} \right\} {P_{f,{n - 1}}\left( \psi_{n} \right)}} + {\left( {1 - P_{\Delta}} \right)P\left\{ {\alpha_{n} = 1} \right\} {P_{f,{n - 1}}\left( {\psi_{n} + \pi} \right)}} + {\frac{P_{\Delta}}{2}P\left\{ {\alpha_{n} = {- 1}} \right\} {P_{f,{n - 1}}\left( {\psi_{n} + \frac{2\pi}{Q}} \right)}} + {\frac{P_{\Delta}}{2}P\left\{ {\alpha_{n} = {- 1}} \right\} {P_{f,{n - 1}}\left( {\psi_{n} - \frac{2\pi}{Q}} \right)}} + {\frac{P_{\Delta}}{2}P\left\{ {\alpha_{n} = 1} \right){P_{f,{n - 1}}\left( {\psi_{n} + \frac{2\pi}{Q} + \pi} \right)}} + {\frac{P_{\Delta}}{2}P\left\{ {\alpha_{n} = 1} \right\} {P_{f,{n - 1}}\left( {\psi_{n} - \frac{2\pi}{Q} + \pi} \right)}}} \right\rbrack}},} & \left( {{eq}.\mspace{14mu} 3.9} \right)\end{matrix}$

where 0<P_(Δ)<1 is a design parameter, optimized depending on the speedof variation of the channel phase H_(n)(ψ_(n)), which is given by

$\begin{matrix}{{{H_{n}\left( \psi_{n} \right)} = {\exp \left\{ {{Re}\left\lbrack {\frac{1}{N_{0}}x_{n}^{j\; \pi \; {h{({n + 1})}}}^{{- j}\; \psi_{n}}} \right\rbrack} \right\}}},} & \left( {{eq}.\mspace{14mu} 3.10} \right)\end{matrix}$

and P{α_(n)=1}, P{α_(n)=1} are the a-priori probabilities (APP) of thesymbols. In the case of a residual frequency error, the parameter P_(A)has to be optimized accordingly. For the purpose of the belowdiscussion, the a-priori probabilities have been set to 0.5, but in acase in which some symbols of the transmitted message are known at thereceiver, it is possible to significantly improve the performance of thedetection algorithm by including the a-priori probabilities of the knownsymbols in the detection process. The PMFs computed during the forwardand backward recursions are employed in the final completion giving thesymbol APPs:

$\begin{matrix}{{P\left( \alpha_{n} \middle| x \right)} = {\sum\limits_{\psi_{n} \in \Psi}{{{P_{b,n}\left( \psi_{n} \right)}\left\lbrack {{\left( {1 - P_{\Delta}} \right){P\left( {\overset{\_}{\alpha}}_{n} \right)}{P_{f,{n - 1}}\left( {\psi_{n} - {\pi {\overset{\_}{\alpha}}_{n}}} \right)}} + {\frac{P_{\Delta}}{2}{P\left( {\overset{\_}{\alpha}}_{n} \right)}{P_{f,{n - 1}}\left( {\psi_{n} - {\pi {\overset{\_}{\alpha}}_{n}} + \frac{2\pi}{Q}} \right)}} + {\frac{P_{\Delta}}{2}{P\left( {\overset{\_}{\alpha}}_{n} \right)}{P_{f,{n - 1}}\left( {\psi_{n} - {\pi {\overset{\_}{\alpha}}_{n}} - \frac{2\pi}{Q}} \right)}}} \right\rbrack}.}}} & \left( {{eq}.\mspace{14mu} 3.11} \right)\end{matrix}$

From the APPs P(α_(n)=1|x) and P(α_(n)=−1|x), the logarithmic likelihoodratio (LLR) L_(n) is computed via

$\begin{matrix}{L_{n} = {\ln {\frac{P\left\{ {\alpha_{n} - 1} \middle| x \right\}}{P\left\{ {\alpha_{n} = \left. {- 1} \middle| x \right.} \right\}}.}}} & \left( {{eq}.\mspace{14mu} 3.12} \right)\end{matrix}$

On the basis thereof, the receiver takes a decision on symbol α_(n) thatis ruled by

{circumflex over (α)}_(n)=sign[L _(n)],

where |L_(n)| is an estimate of the reliability of this decision—thelarger its value the more reliable the corresponding decision.

The algorithm described above is more conveniently implemented in thelogarithmic domain. It turns out that in this case it is required tocompute the logarithm of the sum of exponentials (the Jacobianlogarithm), which results to be

ln(e ^(x) ¹ +e ^(x) ² )=max(x ₁ ,x ₂)+ln(1+e ^(−|x) ¹ ^(-x) ²^(|))≲max(x ₁ ,x ₂).

This detection algorithm is a soft-input soft-output algorithm. Thismeans that an estimate of the ratio between the signal amplitude and thenoise power spectral density (PSD) N₀ must be available (see (eq.3.10)). However, in the absence of channel coding this is not critical.On the contrary, the availability of soft decisions represents apowerful tool to improve the receiver performance. In fact, although achannel coding scheme is not adopted in the AIS scenario, the CRC can beused to improve the performance of the adopted SISO detection algorithmby using the post-processing methods described below.

As becomes apparent from the above, employing the SISO algorithm in thedetection unit 235, each of the determined symbols in the step ofdetermining a sequence of symbols is a symbol that has a highestprobability of being identical to the corresponding transmitted symbol.Thus, operation of the receiver 200 further comprises, for eachdetermined symbol, determining a probability of the determined symbolbeing identical to the corresponding transmitted symbol.

The flow chart of FIG. 11 illustrates the operation of the receiver 200,i.e., a procedure for demodulating a received signal relating to asequence of transmitted symbols that have been modulated by continuousphase modulation, when the SISO algorithm is employed in the detectionunit 235.

At step S1101, a sequence of samples r_(n) is generated from thereceived signal. This step is performed at the front end unit 201, whichin this sense acts as sampling means.

At step S1102, a time offset and a frequency offset of the receivedsignal are estimated on the basis of the sequence of samples. Further,at step S1103, the estimated time offset and the estimated frequencyoffset are used for compensating (correcting) the sequence of samplesfor time and frequency offsets, thereby obtaining a compensated sequenceof samples. Both steps S1102 and S1103 are performed in the timing andfrequency estimator 232, which in this sense acts as estimation means.

At step S1104, a sequence of symbols corresponding to the transmittedsequence of symbols is determined on the basis of the compensatedsequence of samples. Therein, each of the determined symbols is a symbolthat has a highest probability of being identical to the correspondingtransmitted symbol, i.e., each of the determined symbols is a symbol{circumflex over (α)}_(n)=sign [L_(n)] with L_(n) calculated accordingto (eq. 3.12). This step is performed in the detection unit 235, whichin this sense acts as demodulation means.

It is to be noted that FIG. 11 corresponds to a case in which thelimiter 231 has been omitted from the receiver 200. Alternatively, afurther step analogous to step S302 in FIG. 3 relating to anormalization of the received samples could be inserted between stepsS1101 and S1102. In this case, steps S1102 and S1103 would be performedon the normalized sequence of samples.

Post-Processing Stage

Next, two post-processing techniques to be employed by thepost-processing unit 236 of the receiver 200 will be described. It isnoted that such post-processing is not performed in the prior artreceiver disclosed in EP 2 315 366 A1.

Continuous phase modulation is characterized by an intrinsicdifferential encoding. This means that at high signal-to-noise ratio(SNR) values, errors occur in couples (pairs) of consecutive bits.Considering the additional stage of differential encoding foreseen bythe AIS standard, the error patterns at high SNR values are in the form“wcw”, where “w” represents a wrong bit and “c” a correct one. Inaddition, at high SNR values when a packet is wrong, usually a singlecouple of bit errors occurs.

In view of this finding, the present disclosure proposes the followingtwo alternative post-processing procedures to be employed by thepost-processing unit 236 of the receiver 200: bit flipping and syndromedecoding. These post-processing procedures will now be described in moredetail.

First, the bit flipping procedure will be described. When the CRCindicates that a decoded packet is wrong, it is assumed that only onecouple of bits is wrong. Thus, by inverting (reverting) a couple (pair)of bits in the packet that has not been decoded correctly, a correctpacket can be obtained. Here, inverting indicates that each bit of thepair is switched from +1 to −1 or from −1 to +1 (or equivalently from 1to 0 or from 0 to 1), depending on the initial state of the respectivebit. A more detailed account of the bit flipping procedure is now givenwith reference to the flow chart of FIG. 12.

At step S1201, a packet of interest is generated from the decodedsequence of samples received from the detection unit 235. At step S1202,the CRC is performed and a checksum of the packet of interest iscalculated. If the calculated CRC checksum at step S1203 indicates thatthe packet has been decoded (detected) correctly, the packet is outputat step S1208 as a correctly decoded (detected) packet. Otherwise, theflow proceeds to step S1204, at which one or more pairs of symbols inthe packet are inverted. A pair of symbols (bits) corresponds to twosymbols that are separated by a single further symbol. After invertingthe pair of symbols, at step S1205 the CRC is performed again and thechecksum of the packet including the inverted pair of symbols iscalculated. If it is found at step S1206 that the calculated CRCchecksum now indicates a correctly decoded packet, the flow proceeds tostep S1208, at which the packet including the inverted pair of symbolsis output as a correctly decoded packet. Otherwise, the respectivepacket is discarded at step S1207. Alternatively, furtherpost-processing techniques, such as on-ground processing described belowmay be applied to this packet instead of discarding it.

In a modification, having at hand the likelihood of each decoded symbolto be identical to the corresponding original (i.e., transmitted) symbolthat is provided by the SISO algorithm, it is searched for the pair ofbits with the lowest reliability (i.e., the lowest likelihood of beingidentical to the respective original symbols), and the respective pairis inverted at step S1204.

In a further modification, the bit flipping operation may be performedon the two least reliable couples, or until a valid codeword (i.e., acorrectly decoded packet) is found. This case is illustrated by the flowchart of FIG. 13.

At step S1301, a packet of interest is generated from the decodedsequence of samples received from the detection unit 235. At step S1302,the CRC is performed and a checksum of the packet of interest iscalculated. If the calculated CRC checksum at step S1303 indicates thatthe packet has been decoded (detected) correctly, the packet is outputat step S1316 as a correctly decoded (detected) packet. Otherwise, theflow proceeds to step S1304, at which a first pair of symbols having thelowest reliability (i.e., having the lowest likelihood of beingidentical to the original pair of symbols) is determined. At step S1305,a second pair of symbols having the next-to-lowest reliability (i.e.,having the next-to-lowest likelihood of being identical to the originalpair of symbols) is determined.

Then, at step S1306, the first pair is inverted, while the second pairis left untouched. After inverting the first pair of symbols, at stepS1307 the CRC is performed again and the checksum of the packetincluding the inverted first pair of symbols is calculated. If it isfound at step S1308 that the calculated CRC checksum now indicates acorrectly decoded packet, the flow proceeds to step S1316, at which thepacket including the inverted first pair of symbols is output as acorrectly decoded packet. Otherwise, the flow proceeds to step S1309, atwhich the first pair is left in its initial state and the second pair isinverted. After inverting the second pair of symbols, at step S1310 theCRC is performed again and the checksum of the packet including theinverted second pair of symbols is calculated. If it is found at stepS1311 that the calculated CRC checksum now indicates a correctly decodedpacket, the flow proceeds to step S1316, at which the packet includingthe inverted second pair of symbols is output as a correctly decodedpacket. Otherwise, the flow proceeds to step S1312, at which both thefirst pair and the second pair are inverted (with respect to theirrespective initial states). After inverting the first and second pairsof symbols, at step S1313 the CRC is performed again and the checksum ofthe packet including the inverted first and second pairs of symbols iscalculated. If it is found at step S1314 that the calculated CRCchecksum now indicates a correctly decoded packet, the flow proceeds tostep S1316, at which the packet including the inverted first and secondpairs of symbols is output as a correctly decoded packet. Otherwise, theflow proceeds to step S1315, at which the respective packet isdiscarded. Alternatively, further post-processing techniques, such ason-ground processing described below may be applied to this packetinstead of discarding it.

In the above, it is understood that the steps of inverting the firstpair of symbols only, inverting the second pair of symbols only andinverting both the first and second pairs of symbols may beinterchanged, i.e., these steps may be performed in any order.

Next, the syndrome decoding procedure will be described. The syndrome ofany valid codeword (packet) is always equal to a constant value (it isnot zero since the CRC foreseen by the AIS standard is not a linear codedue to the particular employed initialization), and the syndrome of aninvalid codeword depends only on the error sequence, and is independentof the transmitted sequence. For the purposes of the present disclosure,it can be said that the syndrome corresponds to the CRC checksum of thereceived sequence. In order to apply this kind of post-processing, allerror patterns containing one and two couples of wrong bits are testedbeforehand and the corresponding syndromes are saved to a pre-storedtable which indicates a relationship between checksum values (syndromes)and error sequences (error patterns). When receiving a decoded sequenceof samples from the detection unit 235, the following steps illustratedin the flow chart of FIG. 14 are performed.

At step S1401, a packet of interest is generated from the decodedsequence of samples received from the detection unit 235. At step S1402,the CRC (i.e., checksum) is computed for the packet of interest. Here,the CRC checksum corresponds to the syndrome. If it is found at stepS1403 that the computed syndrome equals the syndrome of a correctcodeword (packet), the packet is declared correct and output as acorrectly decoded packet at step S1409. Otherwise, an error sequence(error pattern) is determined by searching for the computed syndromeamong those corresponding to the saved error patterns in the pre-storedtable, starting from those derived from a single incorrect pair ofsymbols. In other words, the error sequence is determined on the basisof the checksum value and the pre-stored table indicating a relationshipbetween checksum values and error sequences. If a correspondence isfound, at step S1405 the respective error sequence is extracted from thetable and the packet is corrected by inverting the respective pair(s)located at positions in the packet indicated by the error sequence. Ifno correspondence is found, the packet is declared incorrect and isdiscarded.

After inverting the pair(s) of symbols indicated by the error sequence,at step S1406 the CRC is performed again and the checksum of the packetincluding the inverted pair(s) of symbols is calculated. If it is foundat step S1407 that the calculated CRC checksum now indicates a correctlydecoded packet, the flow proceeds to step S1409, at which the packetincluding the inverted pair(s) of symbols is output as a correctlydecoded packet. Otherwise, the respective packet is discarded at stepS1408. Alternatively, further post-processing techniques, such ason-ground processing described below may be applied to this packetinstead of discarding it.

To further improve the reliability of syndrome decoding, when searchingfor errors corresponding to two pairs of wrong bits, only those pairs ofsymbols whose LLRs do not exceed a fixed threshold may be corrected.

It has been verified by the inventors that the second post-processingtechnique outperforms the first one.

Post Detection Synchronization

Next, the post-detection synchronization unit and its operation will bedescribed. When a packet is correctly decoded (detected), it can bere-modulated and subtracted from the received signal in order to cancelinterference by this packet and to try to decode (detect) other packets.However, for this purpose the corresponding (time-invariant) amplitudeand (time-varying) channel phase, which are not required for detection,must be estimated. In addition, a refined frequency estimate must bealso computed since the frequency uncertainty after the pre-detectionsynchronization stage is larger than acceptable for a reliableinterference cancellation. As the inventors have found, one of the mostcritical tasks in this respect is represented by the frequencyestimation. In fact, in this case a very large accuracy is required. Inorder to have a limited performance loss with respect to the case ofperfect cancellation, the residual frequency error must be lower than10⁻⁴/T, thus much lower than the frequency error of 10⁻²/T÷1.5·10⁻²/Tthat is tolerated by the detection algorithms described above.

Although data-aided (DA) algorithms based on the whole packet areadopted in the prior art receiver disclosed in EP 2 315 366 A1 forfrequency, (time-invariant) phase and amplitude estimation, anon-negligible performance loss with respect to perfect cancellation isexperienced. In order to improve the performance compared to that of theprior art receiver, the present disclosure proposes the modificationsset out below.

First of all, post-detection synchronization is performed based on theoversampled received signal (i.e., the sequence of samples generatedtherefrom) instead of on the matched filter output. The advantage isthat, contrarily to what happens at the output of the matched filter233, 234, noise is white and inter-symbol interference (ISI) is removed.In other words, it is avoided that ISI and the colored noise degrade theperformance. Therefore, an oversampled version of the detected packet isreconstructed, which is also required for performing cancellation, withtime shift provided by the pre-detection stage and arbitrary amplitudeand phase. This can be achieved through the discrete-time modulator(signal reconstruction unit) 238 and the quadratic interpolator 240.Here, it is not necessary to employ the frequency estimate obtained inthe pre-detection stage, since the post-detection frequency estimator(frequency estimator) 237 that we will now be described has asufficiently large estimation range.

Let {ŝ_(nη+m)} denote the samples of the reconstructed packet. Frequencyestimation is then performed on samples z_(nη+m)=r_(mη+m)ŝ*_(nη+m) byusing the DA Mengali-Morelli algorithm. The use of this algorithm, whichhas the same performance as the algorithm (Luise-Reggiannini algorithm)proposed in M. Luise, R. Reggiannini, Carrier frequency recovery inall-digital modems for burst-mode transmissions, IEEE Trans. Commun.,vol. 43, pp. 1169-1178, March 1995 and employed in the prior artreceiver allows also to remove the main limitation of theLuise-Reggiannini algorithm. In fact, the Luise-Reggiannini algorithmhas an estimation range which depends on the number of symbol intervalsobserved by the estimator—the larger this number the more limited theestimation range. Considering that the initial frequency uncertainty(after the pre-detection stage) is ±1.5·10⁻²/T, the prior art estimatorcan work by using a very limited number of symbol intervals, thusproviding a very limited estimation accuracy.

To address this problem, it is suggested in the prior art to performfrequency synchronization in two steps by using a frequency estimatorworking on a limited number of symbols in the first step and a secondestimator (still based on the Luise-Reggiannini algorithm) working on alarger number of symbols to increase the accuracy. Since the DAMengali-Morelli algorithm has an estimation range larger than ±0.2/Tindependently of the number of observed symbol intervals, the wholepacket can be used to obtain the most accurate estimate. It has beenverified by the inventors that for a given number of observed symbols,the DA Mengali-Morelli algorithm has the same performance as theLuise-Reggiannini algorithm for both the AWGN scenario and theinterference-limited scenario. In addition, employing the DAMengali-Morelli algorithm allows reaching the modified Cramer-Rao lowerbound (MCRB) in the AWGN scenario, so that there is no room for furtherimprovement of the post-detection frequency synchronization. It has alsobeen verified by the inventors that there is no performance loss in thefrequency estimation in the presence of the residual timing error beforepost-detection frequency synchronization.

Post-detection phase and amplitude estimation in the phase and amplitudeestimator 241 can then be performed jointly by using the maximumlikelihood (ML) technique. To simplify the notation, S_(nη+m) denotessample ŝ_(nη+m) after post-detection frequency estimation andcompensation. Denoting by {circumflex over (θ)} and Â the estimates ofphase and amplitude, respectively, the time-varying channel phase isupdated using a DA first-order phase-locked loop (PLL) with error signalgiven by Im[r_(nη+m) S_(nη+m)*e^(−jθ) ^(nη+m) ] whereas the amplitude isestimated as

$\hat{A} = \frac{\left| {{\sum_{n = 0}^{N - 1}{\sum_{m = 0}^{\eta - 1}r_{n\; \eta}}} + {S_{{n\; \eta} + m}^{*}^{{- j}\; {\hat{\theta}}_{{n\; \eta} + m}}}} \right|}{N\; \eta}$

where N is the number of symbol intervals considered for the estimation.To leave out of consideration the initialization of the PLL, a forwardand a backward PLL can be employed. The forward PLL is used to estimatethe phase in the second half of a packet, whereas the backward PLL isemployed to estimate the phase in the first half of the packet.

Since the complexity is very limited, estimates based on the wholepacket are considered. Contrary to the situation in the prior art, thereis now no need to use non-coherent post detection integration to performthe amplitude estimation, since post-detection frequency estimation andcompensation has already been performed and an algorithm that is robustwith respect to uncompensated frequency offsets is not required.

It is has been observed by the inventors that interference cancellationcan be improved, thus obtaining a performance improvement, when timingestimation is refined after post-detection frequency estimation andcompensation. According to the present disclosure, this task isperformed in the quadratic interpolator 240 using the following DAalgorithm. The quadratic interpolator 240 performs both the timingestimation and subsequent quadratic interpolation.

First, the following quantities are computed:

$\gamma_{0} = {\sum\limits_{n = 0}^{N - 1}\; {\sum\limits_{m = 0}^{\eta - 1}\; {r_{{n\; \eta} + m}S_{{n\; \eta} + m}^{*}^{{- j}\; {\hat{\theta}}_{{n\; \eta} + m}}}}}$$\gamma_{1} = {\sum\limits_{n = 0}^{N - 1}\; {\sum\limits_{m = 0}^{\eta - 1}\; {r_{{n\; \eta} + m}S_{{n\; \eta} + m + 1}^{*}^{{- j}\; {\hat{\theta}}_{{n\; \eta} + m + 1}}}}}$$\gamma_{- 1} = {\sum\limits_{n = 0}^{N - 1}\; {\sum\limits_{m = 0}^{\eta - 1}\; {r_{{n\; \eta} + m}S_{{n\; \eta} + m - 1}^{*}^{{- j}\; {\hat{\theta}}_{{n\; \eta} + m - 1}}}}}$

The refined timing estimate is computed in closed form as

$\tau = {\frac{\eta}{T}{\frac{{Re}\left\lbrack {{\gamma_{0}^{*}\left( {\gamma_{1} - \gamma_{2}} \right)}\text{/}2} \right\rbrack}{\frac{\left| {\gamma_{1} - \gamma_{- 1}} \right|^{2}}{4} + {{Re}\left\lbrack {\gamma_{0}^{*}\left( {\gamma_{1} + y_{- 1} - {2\gamma_{0}}} \right)} \right\rbrack}}.}}$

The above evaluation should be performed if this timing refinement andthe following quadratic interpolation have a complexity which deservesto be spent considering the resulting performance improvement. As can beseen from FIG. 2, the post-detection estimation must be performed usingthe samples before the limiter 231.

Finally, it is mentioned that in the AIS standard, a few symbols oframp-up and ramp-down are foreseen at the beginning and at the end of apacket. This fact must be taken into account during the cancellation,i.e., the reconstructed signal must have appropriate ramp-up andramp-down intervals. From the analysis of real received AIS packets, thepower profile corresponding to the ramp-up and ramp-down sections can bedetermined. Hence, it is possible to estimate the parameters of thepower profile and to reconstruct the waveform by combining theseestimated profiles with the reconstructed packet that has beenreconstructed on the basis of the detected symbols.

The flow chart of FIG. 15 illustrates the operation of the receiverincluding post-detection synchronization and interference cancellation.Steps S1501 to S1505 correspond to steps S301 to S305 illustrated inFIG. 3, respectively. At step S1506, correctly decoded (detected)packets are identified and output from the receiver. At step 1507, theidentified correctly decoded packets are canceled from the sequence ofsamples by the subtractor 243 before input to the limiter 231 (cf., FIG.2), in the manner described above. Then, the flow proceeds to step S1501to perform demodulation of the sequence of samples from which alreadydecoded packets have been cancelled. Subsequently, it may be attemptedto decode further packets the decoding of which had not been possiblebefore because of interfering packets. Steps S1501 to 1507 may berepeated as often as necessary to decode all packets, or until apredetermined count for repeating these steps has been reached. It isunderstood that before performing step S1506, the method may involvefurther steps relating to the post-processing described above, such asbit flipping or syndrome decoding.

Frame Synchronization

Frame synchronization is performed by computing the CRC checksum for the128 possible positions of the start of a message. When the rightposition is found, the CRC is verified, the search is stopped and thesuccessfully decoded message is passed on to the message parser block204, which has the functions of discarding duplicated messages andpassing the successfully detected messages to the signal reconstructionblock of each zonal demodulator. In order to lower the probability offalse alarms down to an acceptable value and to reduce the complexity, apreliminary start flag and end flag verification is also performed. Thisprocedure does not change in the presence of bit stuffing. In fact, inthe AIS standard, it is foreseen that if five consecutive 1's are foundin the bit stream to be transmitted, a 0 should be inserted after thefive consecutive 1's. As a consequence, at the receiver, when fiveconsecutive 1's are found followed by a 0, the burst length must beincreased by one and the initial bit of the CRC field must be translatedaccordingly.

Finally, it is pointed out that the false alarm probability of the aboveframe synchronization procedure is independent of the adopted detector.In fact, the false alarm probability in the context of the presentapplication corresponds to the probability that a sequence of randomlygenerated bits satisfies the CRC and the start flag and end flagverification.

Receiver Performance

In FIG. 17, a performance comparison between the prior art receiverdisclosed in EP 2 315 366 A1 and the receiver disclosed herein is shownfor a single interferer with different values of signal-to-interferencepower ratio (SIR). Both the useful signal and the interferer have arandom normalized Doppler frequency uniformly distributed in theinterval [0,0.22], and the receiver disclosed herein employs the fourthtiming and frequency estimation algorithm and the second post-processingtechnique (syndrome decoding). In the figure, the horizontal axisindicates the signal-to-noise ratio (SNR) in units of dB, and thevertical axis indicates the common logarithm of the packet error rate(PER). Graphs 1701, 1703, 1705 indicate the performance of the prior artreceiver for SIRs of 5 dB, 10 dB and in the absence of interference,respectively. Graphs 1702, 1704, 1706 indicate the performance of thereceiver disclosed herein for SIRs of 5 dB, 10 dB and in the absence ofinterference, respectively. As can be seen from a comparison ofcorresponding graphs, the presently-disclosed receiver excels inperformance (lower PER) for all values of SIR.

On-Ground Processing

The whole process of conveying information from one point to anotherreduces to the ability of the receiver to extract the data sent by thetransmitter. In digital communications, the existence of a-prioriinformation about the incoming data can assist its extraction, henceimproving the receiver performance. Whether or not such a-prioriinformation exists is system dependent. In the AIS, such informationexists to a certain degree, and the present disclosure further proposesa mechanism for exploiting availability of a-priori information, ifrequired. This mechanism may be employed to the fullest advantageon-ground where the required a-priori information and computationalpower are available, but is not limited to such an implementation.

Unlike prior art receivers that dismiss (discard) packets that could notbe decoded, in the present disclosure their decoding is re-attempted,but this time using the available a-priori information. This way,computational resources are saved when not needed and spared for thosecases in which the SIR is low enough to make the unassisted decodingprocess fail. Thus, whenever the receiver cannot decode a packet in afirst (data unassisted) attempt it will follow the procedure describedbelow in order to retrieve the available a-priori information for asecond (this time data assisted) attempt. A packet is discarded onlywhen also the latter attempt fails.

A process flow of a procedure for data assisted decoding is illustratedin FIG. 16. While the below description makes exemplary reference to anAIS receiver aboard a satellite, the disclosure shall not be limited toreceivers aboard satellites, and shall extend to alternative locationsfor installation of the receiver. Moreover, in the followingdescription, it will be referred to on-ground processing, in which casea packet that could not be decoded is transmitted to a remote(on-ground) processing site. However, the present disclosure shall notbe limited to this case, and shall in particular comprise the case thatthe packet is not transmitted to a remote processing site, but isprocessed in the receiver in accordance with the below procedure.Although in this case the term “data aided processing” would be moreappropriate, the below description nevertheless only refers to on-groundprocessing, for reasons of conciseness.

Whenever a packet arrives at the receiver, it is tried to decode it. Incase of failure to decode the packet, at step S1601 a reception timingof the packet at which the packet has been received is determined bylooking up the time at which, e.g., the satellite has received thecorresponding data.

At step S1602, the determined reception timing is used along with thesatellite's ephemeris to estimate the satellite's field of view at thereception timing. If the receiver is installed on a naval vessel or anyother sea- or earthbound object, the location of the respective objectis taken into account instead of the satellite's ephemeris. If thereceiver is installed at a fixed position, steps S1601 and S1602 may beomitted, and the receiver's fixed field of view may be looked up, e.g.,from a database.

At step S1603, a list of potential transmitters of the received packetis obtained by referring to a database containing the latest knownposition of all naval vessels (transmitting objects) and identifyingthose that could have been in sight of the satellite at the receptiontiming. These naval vessels form a set of potential transmitters.

At step S1604, the MMSI of each naval vessel that has been in sight ofthe satellite is correlated with the received packet, or with at leastthe MMSI field of the received packet.

At step S1605, the available a-priori data (previously obtained data) isobtained by retrieving the available a-priori data of those MMSIs forwhich the correlation obtained at step S1604 is above a predeterminedthreshold. If the correlation is not above the threshold for any of theMMSIs of the potential transmitters, the packet is discarded.

At step S1606, the obtained a-priori data (previously obtained data) isused to aid demodulation (decoding) of the received packet for each MMSIfor which the correlation has been above the predetermined threshold.Whenever the decoder succeeds in decoding, the process stops and theinformation of the decoded packet is extracted. Otherwise the receivedpacket is discarded.

As indicated above, steps S1601 to S1606 may be performed either at aremote (on-ground) processing site, or in the receiver itself, whereinin the former case the procedure further comprises a transmission stepof transmitting the received packet to the processing site.

The above technique uses a-priori known information to assist thedecoding process when the receiver fails to recover the new position ofa naval vessel (i.e., of the specific MMSI). That information consistsof the 70 bits corresponding to: a training sequence (24 bits), a startflag (8 bits), a user-ID (30 bits) and an end flag (8 bits). On top ofthese, the particular nature of the AIS allows to use some additionalbits coming from the latitude and longitude fields as a-prioriinformation, although not in a straightforward manner. Their number andvalue (along with the corresponding confidence level) can be determinedbased on the latest reported position, speed and heading of the navalvessel.

The position information is by nature highly correlated. In other words,the coordinates of two points that lie close together are expected to besimilar. Given the small distance traveled by a naval vessel within thetime span between two consecutive AIS reports, the receiver can assume avalue for the latitude/longitude (lat/lon) main significant bits withcertain confidence level and use that information as a-prioriinformation to assist the decoding process.

The AIS is a memory-less system where all the information about theposition of a naval vessel is contained in the latest successful report.As soon as this report is received, the location of the naval vessel isperfectly determined and corresponds to a point with virtually nouncertainty. As time passes, this point transforms into a growing regionrepresenting the position uncertainty due to the movement of the navalvessel. This area is defined as the Search and Rescue (SAR) region inwhich the probability of finding the naval vessel is 100%.

However, a naval vessel not only reports its position, but also itsspeed and heading, hence the probability of finding the naval vesselwithin the SAR region is not uniform. Instead, the naval vessel isexpected to be at certain point according to a certain navigation planor certain navigation criteria, although its actual position is stillunknown. The expected position on its own is meaningless unless it comesalong with a probability density function (PDF). The PDF determines theconfidence level of the prediction.

The SAR region and the PDF in the SAR region will now be explained inmore detail by way of an example. Let t be the elapsed time since thelatest report from a given naval vessel. With the latest report, thenaval vessel had informed the AIS to be at certain position p₀ withheading h₀ at speed v₀. For the sake of simplicity is assumed that theSAR region is a circle centered in p₀ with radius R_(SAR) (over theearth's surface). The radius may be established according to certaincriteria, for example, R_(SAR)=v_(max)t where v_(max) is the navalvessel's maximum speed, but other variables such as sea currents may bealso accounted for. Assuming that the naval vessel follows anorthodromic route passing through p₀ with heading h₀ and that thedistance traveled in t is presumably d=v₀t, then the expected positionp(t) is easily determined. As indicated above, the expected positionp(t) must come along with an associated PDF. A Gaussian function asindicated in (eq. 3.13) which is centered in p(t) and varies with thegeographical distance (i.e., distance over the Earth's surface) r(p) tothis point seems a reasonable choice. The variance of the Gaussianfunction is chosen so that p₀ lies in the 3σ circle (3σ=d).

If, for instance, a naval vessel is considered that has reported to beat 25° N 45° W, heading 45° at a speed of 25 kn four hours ago (example1), its current expected position is 26.1732563015498° N43.6878131578038° W. The encoded fields in this case are given by000111011111001111110010001 and 1110011100000000011010110001,respectively.

$\begin{matrix}{{f_{}(p)} = {{K_{}^{- \frac{{r{(p)}}^{2}}{2\sigma^{2}}}\mspace{14mu} {with}\mspace{14mu} K_{}} = \frac{1}{\int{\int_{SAR}^{- \frac{{r{(p)}}^{2}}{2\sigma^{2}}}}}}} & \left( {{eq}.\mspace{14mu} 3.13} \right)\end{matrix}$

The question now is how confident one can be in the correctness of thevalues of these bits. Using the PDF it is fairly simple to make a goodestimation for the most significant bits, wherein it has to be notedthat although the numbers are easily calculable, the results are casedependent.

If, for instance, a naval vessel is considered that has reported to beat 26° N 92° W (Gulf of Mexico), heading 135° at a speed of 25 kn twohours ago (example 2), the number of bits with high certainty is biggersince the elapsed time is shorter and therefore the uncertainty issmaller. A similar result is expected if the naval vessel is travelingin a small bounded region such as the Black Sea.

More realistic modeling is possible if traffic information is also takeninto account. This may be done through a weighted sum of terms. If, forinstance, a naval vessel is considered that has reported its position atthe Bay of Bengal 6.25° N 90° E, heading 90° at a speed of 25 kn twohours ago (example 3), taking into account the traffic patterns in thatarea, the PDF given in (eq. 3.14) is obtained where f_(g) (p) is theGaussian function, f_(t)(p) is a function representing the traffic,K_(t) is a weighting constant and K_(N) is a normalization constant.When including the traffic, it makes sense to re-compute the expectedposition to be the expected position of the PDF rather than the centerof the Gaussian function.

$\begin{matrix}{{{f_{pdf}(p)} = {{K_{N}\left( {1 + {K_{t}{f_{t}(p)}}} \right)}{f_{}(p)}}},{{{with}\mspace{14mu} K_{N}} = {\frac{1}{\int{\int_{SAR}{\left( {1 + {K_{t}{f_{t}(p)}}} \right){f_{}(p)}}}}.}}} & \left( {{eq}.\mspace{14mu} 3.14} \right)\end{matrix}$

For the particular case of the naval vessel in the Bay of Bengal, andfor typical traffic patterns in that area, the inclusion of the traffichelps to narrow down the area where there is a high probability offinding the naval vessel, hence the results are better. However it mayalso happen that the inclusion of the traffic broadens this area and theresults are worsened. Thus, it is eventually decisive how faithful toreality the used PDF is.

Lastly, the performance gain obtainable from the adoption of thedescribed on-ground processing is illustrated in FIGS. 18A and 18B. Toperform realistic simulations, however, it is necessary to take intoaccount the bit permutation and the Non Return to Zero (NRZI) encodingforeseen by the AIS standard. In particular, the 168 data bits are splitinto octets, and then, the octets are left in the original order, butwith the bit order reversed inside each single octet. After thisoperation, the whole packet is subjected to NRZI encoding, in which eachtransmitted symbol results from

s _(i) =c _(i) +c _(i-1)+1,  (eq. 4.1)

where c_(i) and c_(i-1) are two consecutive bits of the packet and thesum is intended to be modulo 2. From (eq. 4.1) it is clear that thesymbol s_(i) is incorrect if and only if a single bit error is presenton c_(i) or on c_(i-1). Thus, the error probability of s_(i) can beexpressed as

P{s _(i) ≠s _(i) }=P{c _(i) }P{c _(i) }P{c _(i-1) =c _(i-1) }+P{c ₁ ≠c_(i) }P{c _(i-1) ≠c _(i-1)},  (eq. 4.2)

where s _(i), c _(i), c _(i-1) represent the correctly transmittedvalues.

FIG. 18A illustrates the performance of the method disclosed herein withand without on ground processing for the case of the naval vessel ofexample 1, i.e., a naval vessel in the open sea, for different values ofSIR. FIG. 18B illustrates the performance of the method with and withouton ground processing for the case of the naval vessel of example 2,i.e., a naval vessel in Gulf of Mexico, for different values of SIR.Both the useful signal and the interferer have a random normalizedDoppler frequency uniformly distributed in the interval [0,0.22]. As inFIG. 17, the horizontal axis indicates the signal-to-noise ratio (SNR)in units of dB, and the vertical axis indicates the common logarithm ofthe packet error rate (PER). Graphs 1801, 1811 indicate the performanceof the method without on-ground processing for a SIR of 5 dB for example1 and example 2, respectively, and graphs 1802, 1812 indicate theperformance of the method with on-ground processing for a SIR of 5 dBfor example 1 and example 2, respectively. Graphs 1803, 1813 indicatethe performance of the method without on-ground processing for a SIR of10 dB for example 1 and example 2, respectively, and graphs 1804, 1814indicate the performance of the method with on-ground processing for aSIR of 10 dB for example 1 and example 2, respectively. Graphs 1805,1815 indicate the performance of the method without on-ground processingin the absence of interference for example 1 and example 2,respectively, and graphs 1806, 1816 indicate the performance of themethod with on-ground processing in the absence of interference forexample 1 and example 2, respectively. As can be seen from a comparisonof corresponding graphs, the presently-disclosed receiver with on-groundprocessing excels in performance (lower PER) for all values of SIR andfor both example 1 and example 2.

Features, components and specific details of the structures of theabove-described embodiments may be exchanged or combined to form furtherembodiments optimized for the respective application. As far as thosemodifications are readily apparent for an expert skilled in the art,they shall be disclosed implicitly by the above description withoutspecifying explicitly every possible combination, for the sake ofconciseness of the present description.

1. A method for demodulating a received signal relating to a sequence oftransmitted symbols that have been modulated by continuous phasemodulation, the method comprising the steps of: A) normalizing samplesof a sequence of samples generated from the received signal, to obtain anormalized sequence of samples, wherein an amplitude of each sample ofthe normalized sequence of samples has an absolute value equal to unity;B) estimating, on the basis of the normalized sequence of samples, atime offset and a frequency offset of the received signal and using theestimated time offset and the estimated frequency offset forcompensating the normalized sequence of samples for the time andfrequency offsets to obtain a compensated sequence of samples; and C)determining a sequence of symbols corresponding to the transmittedsequence of symbols on the basis of the compensated sequence of samples.2. The method according to claim 1, wherein the estimate of the timeoffset and the estimate of the frequency offset are determined using afeed-forward algorithm that involves performing an auto-correlation of asequence of samples input to the algorithm.
 3. The method according toclaim 1, wherein estimating the time offset and the frequency offsetinvolves: filtering the normalized sequence of samples using a low-passfilter to obtain a filtered sequence of samples; determining theestimate of the time offset on the basis of a first result obtained byauto-correlating the filtered sequence of samples; determining theestimate of the frequency offset on the basis of a second resultobtained by auto-correlating the filtered sequence of samples or a firstsequence of samples derived from the normalized sequence of samples;interpolating the normalized sequence of samples or a second sequence ofsamples derived from the normalized sequence of samples on the basis ofthe estimate of the time offset; and compensating the normalizedsequence of samples or a third sequence of samples derived from thenormalized sequence of samples for the frequency offset using theestimate of the frequency offset, to obtain the compensated sequence ofsamples.
 4. The method according to claim 1, wherein estimating the timeoffset and the frequency offset involves: B1) filtering the normalizedsequence of samples using a first low-pass filter to obtain a firstfiltered sequence of samples; B2) determining the estimate of the timeoffset on the basis of a first result obtained by auto-correlating thefirst filtered sequence of samples; B3) determining a first estimate ofthe frequency offset on the basis of the first result; and B4)compensating the normalized sequence of samples for the frequency offsetusing the first estimate of the frequency offset, to obtain a firstcompensated sequence of samples.
 5. The method according to claim 4,wherein estimating the time offset and the frequency offset furtherinvolves: B5) filtering the first compensated sequence of samples usinga second low-pass filter to obtain a second filtered sequence ofsamples; B6) determining a second estimate of the frequency offset onthe basis of a second result obtained by auto-correlating the secondfiltered sequence of samples; B7) compensating the first compensatedsequence of samples for the frequency offset using the second estimateof the frequency offset, to obtain a second compensated sequence ofsamples; and B8) interpolating the second compensated sequence ofsamples on the basis of the estimate of the time offset to obtain thecompensated sequence of samples.
 6. The method according to claim 1,wherein estimating the time offset and the frequency offset involves:B1) filtering the normalized sequence of samples using a first low-passfilter to obtain a first filtered sequence of samples; B2) determiningthe estimate of the time offset on the basis of a first result obtainedby auto-correlating the first filtered sequence of samples; and B3)interpolating the normalized sequence of samples on the basis of theestimate of the time offset to obtain an interpolated sequence ofsamples.
 7. The method according to claim 6, wherein estimating the timeoffset and the frequency offset further involves: B4) filtering theinterpolated sequence of samples using a second low-pass filter toobtain a second filtered sequence of samples; B5) down-sampling thesecond filtered sequence of samples to obtain a first down-sampledsequence of samples; B6) determining a first estimate of the frequencyoffset on the basis of a second result obtained by auto-correlating thefirst down-sampled sequence of samples; and B7) compensating theinterpolated sequence of samples for the frequency offset using thefirst estimate of the frequency offset, to obtain a first compensatedsequence of samples.
 8. The method according to claim 7, whereinestimating the time offset and the frequency offset further involves:B8) filtering the first compensated sequence of samples using a thirdlow-pass filter to obtain a third filtered sequence of samples; B9)determining a second estimate of the frequency offset on the basis of athird result obtained by auto-correlating the third filtered sequence ofsamples; and B10) compensating the first compensated sequence of samplesfor the frequency offset using the second estimate of the frequencyoffset, to obtain the compensated sequence of samples.
 9. The methodaccording to claim 7, wherein the first result is obtained by applying afirst auto-correlation algorithm to the first filtered sequence ofsamples; the second result is obtained by applying a secondauto-correlation algorithm that is different from the firstauto-correlation algorithm to the first down-sampled sequence ofsamples; and the first compensated sequence of samples is thecompensated sequence of samples.
 10. The method according to claim 8,wherein the first result is obtained by applying a firstauto-correlation algorithm to the first filtered sequence of samples;and the second result is obtained by applying the first auto-correlationalgorithm to the down-sampled sequence of samples.
 11. The methodaccording to claim 8, wherein the first result is obtained by applying afirst auto-correlation algorithm to the first filtered sequence ofsamples; and the second result is obtained by applying a secondauto-correlation algorithm that is different from the firstauto-correlation algorithm to the first down-sampled sequence ofsamples.
 12. The method according to claim wherein the third result isobtained by applying the first auto-correlation algorithm to the thirdfiltered sequence of samples.
 13. The method according to claim 1,wherein in the step of determining the sequence of symbols, each of thedetermined symbols is a symbol that has a highest probability of beingidentical to the corresponding transmitted symbol.
 14. A method fordemodulating a received signal relating to a sequence of transmittedsymbols that have been modulated by continuous phase modulation, themethod comprising the steps of: A) estimating, on the basis of asequence of samples generated from the received signal, a time offsetand a frequency offset of the received signal and using the estimatedtime offset and the estimated frequency offset for compensating thesequence of samples for the time and frequency offsets, to obtain acompensated sequence of samples; and B) determining a sequence ofsymbols corresponding to the transmitted sequence of symbols on thebasis of the compensated sequence of samples, wherein each of thedetermined symbols is a symbol that has a highest probability of beingidentical to the corresponding transmitted symbol.
 15. The methodaccording to claim 14, further comprising, for each determined symbol,determining a probability of the determined symbol being identical tothe corresponding transmitted symbol.
 16. The method according to atclaim 14, further comprising: generating a packet from the determinedsequence of symbols; calculating a checksum for the packet; and if thechecksum indicates that the packet has not been decoded correctly,inverting a pair of symbols in the packet, wherein the two symbols ofthe pair of symbols are separated by a further symbol.
 17. The methodaccording to claim 16, further comprising: in the packet that has beenjudged as not decoded correctly, determining a first pair of symbolshaving the lowest probability of being identical to the correspondingtransmitted symbols; and inverting the symbols of the determined firstpair of symbols.
 18. The method according to claim 16, furthercomprising: in the packet that has been judged as not decoded correctly,determining a first pair of symbols having the lowest probability ofbeing identical to the corresponding transmitted symbols and a secondpair of symbols having the next-to-lowest probability of being identicalto the corresponding transmitted symbols; and inverting, not necessarilyin this order, the symbols of the first pair only, the symbols of thesecond pair only, and the symbols of the first and second pairssimultaneously, until the checksum of the resulting packet indicatesthat the resulting packet has been decoded correctly.
 19. The methodaccording to claim 16, further comprising: for the packet that has beenjudged as not decoded correctly, determining an error sequence on thebasis of the checksum and a pre-stored table indicating a relationshipbetween checksum values and error sequences; and inverting pairs ofsymbols that are located in the packet at positions indicated by theerror sequence.
 20. The method according to claim 1, wherein thesequence of samples has a first ratio of samples per transmitted symbol;and the method further comprises: down-sampling the compensated sequenceof samples to obtain a down-sampled sequence of samples, thedown-sampled sequence of samples having a second ratio of samples pertransmitted symbol lower than the first ratio of samples; anddetermining the sequence of symbols corresponding to the transmittedsequence of symbols on the basis of the down-sampled sequence ofsamples.
 21. The method according to claim 20, wherein the first ratiois 3 or more, and the second ratio is
 1. 22. The method according toclaim 1, further comprising: D) identifying packets of symbols that havebeen decoded correctly; E) cancelling said correctly decoded packetsfrom the sequence of samples by subtracting, from the sequence ofsamples, a reconstructed signal that has been reconstructed from saidcorrectly decoded packets to obtain an interference-cancelled sequenceof samples; and repeating the steps of the method according to the atleast one of the preceding claims for the interference-cancelledsequence of symbols.
 23. The method according to claim 1, furthercomprising, if decoding a packet of symbols has failed, determining areception timing at which the respective packet of symbols has beenreceived; determining the field of view from which signals could havebeen received at the reception timing; obtaining a list of potentialtransmitters that have been in the field of view at the receptiontiming; and correlating, for each of the potential transmitters, anidentifier of the respective potential transmitter of the packet ofsymbols for which decoding has failed with said packet to obtain acorrelation value; obtaining previously obtained data relating to eachof the potential transmitters for which the correlation value is above apredetermined threshold; and decoding said packet of symbols using thepreviously obtained data.
 24. A receiver for demodulating a receivedsignal relating to a sequence of transmitted symbols that have beenmodulated by continuous phase modulation, the receiver comprising:normalization means for normalizing samples of a sequence of samplesgenerated from the received signal, to obtain a normalized sequence ofsamples, wherein an amplitude of each sample of the normalized sequenceof samples has an absolute value equal to unity; estimation means forestimating, on the basis of the normalized sequence of samples, a timeoffset and a frequency offset of the received signal, and using theestimated time offset and the estimated frequency offset forcompensating the normalized sequence of samples for the time andfrequency offsets, to obtain a compensated sequence of samples; anddecoding means for determining a sequence of symbols corresponding tothe transmitted sequence of symbols on the basis of the compensatedsequence of samples.
 25. The receiver according to claim 24, wherein theestimation means comprises: a first low-pass filter for filtering thenormalized sequence of samples to obtain a first filtered sequence ofsamples; time offset estimation means for determining the estimate ofthe time offset on the basis of a first result obtained byauto-correlating the first filtered sequence of samples; first frequencyoffset estimation means for determining a first estimate of thefrequency offset on the basis of the first result; and firstcompensation means for compensating the normalized sequence of samplesfor the frequency offset using the first estimate of the frequencyoffset, to obtain a first compensated sequence of samples.
 26. Thereceiver according to claim 25, wherein the estimation means furthercomprises: a second low-pass filter for filtering the first compensatedsequence of samples to obtain a second filtered sequence of samples;second frequency offset estimation means for determining a secondestimate of the frequency offset on the basis of a second resultobtained by auto-correlating the second filtered sequence of samples;second compensation means for compensating the first compensatedsequence of samples for the frequency offset using the second estimateof the frequency offset, to obtain a second compensated sequence ofsamples; and interpolation means for interpolating the secondcompensated sequence of samples on the basis of the estimate of the timeoffset to obtain the compensated sequence of samples.
 27. The receiveraccording to claim 24, wherein the estimation means comprises: a firstlow-pass filter for filtering the normalized sequence of samples toobtain a first filtered sequence of samples; time offset estimationmeans for determining the estimate of the time offset on the basis of afirst result obtained by auto-correlating the first filtered sequence ofsamples; and interpolation means for interpolating the normalizedsequence of samples on the basis of the estimate of the time offset toobtain an interpolated sequence of samples.
 28. The receiver accordingto claim 27, wherein the estimation means further comprises: a secondlow-pass filter for filtering the interpolated sequence of samples toobtain a second filtered sequence of samples; down-sampling means fordown-sampling the second filtered sequence of samples to obtain a firstdown-sampled sequence of samples; first frequency offset estimationmeans for determining a first estimate of the frequency offset on thebasis of a second result obtained by auto-correlating the firstdown-sampled sequence of samples; and first compensation means forcompensating the interpolated sequence of samples for the frequencyoffset using the first estimate of the frequency offset, to obtain afirst compensated sequence of samples.
 29. The receiver according toclaim 28, wherein the estimation means further comprises: a thirdlow-pass filter for filtering the first compensated sequence of samplesto obtain a third filtered sequence of samples; second frequency offsetestimation means for determining a second estimate of the frequencyoffset on the basis of a third result obtained by auto-correlating thethird filtered sequence of samples; and second compensation means forcompensating the first compensated sequence of samples for the frequencyoffset using the second estimate of the frequency offset, to obtain thecompensated sequence of samples.
 30. The receiver according to claim 24,wherein the decoding means is configured to determine the sequence ofsymbols such that each of the determined symbols is a symbol that has ahighest probability of being identical to the corresponding transmittedsymbol.
 31. A receiver for demodulating a received signal relating to asequence of transmitted symbols that have been modulated by continuousphase modulation, the receiver comprising: estimation means forestimating, on the basis of a sequence of samples generated from thereceived signal, a time offset and a frequency offset of the receivedsignal, and using the estimated time offset and the estimated frequencyoffset for compensating the sequence of samples for the time andfrequency offsets, to obtain a compensated sequence of samples; anddecoding means for determining a sequence of symbols corresponding tothe transmitted sequence of symbols from the compensated sequence ofsamples, wherein each of the determined symbols is a symbol that has ahighest probability of being identical to the corresponding transmittedsymbol.
 32. The receiver according to claim 31, wherein the decodingmeans is further configured to determine, for each determined symbol, aprobability of the determined symbol being identical to thecorresponding transmitted symbol.
 33. The receiver according to claim31, further comprising: packet generating means for generating a packetfrom the determined sequence of symbols; checksum calculating means forcalculating a checksum for the packet; and inverting means forinverting, if the checksum indicates that the packet has not beendecoded correctly, a pair of symbols in the packet, wherein the twosymbols of the pair of symbols are separated by a further symbol. 34.The receiver according to claim 33, further comprising means fordetermining a first pair of symbols having the lowest probability ofbeing identical to the corresponding transmitted symbols in the packetthat has been judged as not decoded correctly, wherein the invertingmeans is further configured to invert the symbols of the determinedfirst pair of symbols.
 35. The receiver according to claim 33, furthercomprising means for determining a first pair of symbols having thelowest probability of being identical to the corresponding transmittedsymbols and a second pair of symbols having the next-to-lowestprobability of being identical to the corresponding transmitted symbolsin the packet that has been judged as not decoded correctly, wherein theinverting means is configured to invert, not necessarily in this order,the symbols of the first pair only, the symbols of the second pair only,and the symbols of the first and second pairs simultaneously, until thechecksum of the resulting packet indicates that the resulting packet hasbeen decoded correctly.
 36. The receiver according to claim 33, furthercomprising means for determining, for the packet that has been judged asnot decoded correctly, an error sequence on the basis of the checksumand a pre-stored table indicating a relationship between checksum valuesand error sequences, wherein the inverting means is further configuredfor inverting pairs of symbols that are located in the packet atpositions indicated by the error sequence.
 37. The receiver according toat claim 24, wherein the sequence of samples has a first ratio ofsamples per transmitted symbol; the receiver further comprisesdown-sampling means down-sampling the compensated sequence of samples toobtain a down-sampled sequence of samples, the down-sampled sequence ofsamples having a second ratio of samples per transmitted symbol lowerthan the first ratio of samples; and the decoding means is configured todetermine the sequence of symbols corresponding to the transmittedsequence of symbols on the basis of the down-sampled sequence ofsamples.
 38. The receiver according to claim 37, wherein the first ratiois 3 or more, and the second ratio is
 1. 39. The receiver according toclaim 24, further comprising: means for identifying packets of symbolswhich have been correctly decoded; and cancellation means for cancellingsaid correctly decoded packets from the sequence of symbols bysubtracting, from the sequence of symbols, a reconstructed sequence ofsymbols that has been reconstructed from said correctly decoded packets,to obtain an interference-cancelled sequence of samples to be used forfurther demodulation processing.